Discriminant function analysis is a statistical analysis to predict a categorical dependent variable (called a grouping variable) by one or more continuous or binary independent variables (called predictor variables). The original dichotomous discriminant analysis was developed by Sir Ronald Fisher in 1936. It is different from an ANOVA or MANOVA, which is used to predict one (ANOVA) or multiple (MANOVA) continuous dependent variables by one or more independent categorical variables. Discriminant function analysis is useful in determining whether a set of variables is effective in predicting category membership. Discriminant analysis is used when groups are known a priori (unlike in cluster analysis). Each case must have a score on one or more quantitative predictor measures, and a score on a group measure. In simple terms, discriminant function analysis is classification - the act of distributing things into groups, classes or categories of the same type. Moreover, it is a useful follow-up ...

Objectives: In order to identify genes with the greatest contribution to bladder cancer, we proposed a sparse model making the best discrimination from other patients. Methods: In a cross-sectional study, 22 genes with a key role in most cancers were considered in 21 bladder cancer patients and 14 participants of the same age (± 3 years) without bladder cancer in Shiraz city, Southern Iran. Real time-PCR was carried out using SYBR Green and for each of the 22 target genes 2-Δct as a quantitative index of gene expression was reported. We determined the most affective genes for the discriminant vector by applying penalized linear discriminant analysis using LASSO penalties. All the analyses were performed using SPSS version 18 and the penalized LDA package in R.3.1.3 software. Results: Using penalized linear discriminant analysis led to elimination of 13 less important genes. Considering the simultaneous effects of 22 genes with important influence on many cancers, it was found that TGFβ, IL12A, Her2

Linear discriminant analysis (LDA) is a generalization of Fishers linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.. LDA is closely related to analysis of variance (ANOVA) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements.[1][2] However, ANOVA uses categorical independent variables and a continuous dependent variable, whereas discriminant analysis has continuous independent variables and a categorical dependent variable (i.e. the class label).[3] Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain a categorical variable by the values of continuous independent variables. ...

Both Bayesian analysis assuming independence and discriminant function analysis have been used to estimate probabilities of coronary disease. To compare their relative accuracy, we submitted 303 subjects referred for coronary angiography to stress electrocardiography, thallium scintigraphy, and cine fluoroscopy. Severe angiographic disease was defined as at least one greater than 50% occlusion of a major vessel. Four calculations were done: (1) Bayesian analysis using literature estimates of pretest probabilities, sensitivities, and specificities was applied to the clinical and test data of a randomly selected subgroup (group I, 151 patients) to calculate posttest probabilities. (2) Bayesian analysis using literature estimates of pretest probabilities (but with sensitivities and specificities derived from the remaining 152 subjects [group II]) was applied to group I data to estimate posttest probabilities. (3) A discriminant function with logistic regression coefficients derived from the ...

Twenty-three relapsing remitting multiple sclerosis (RRMS) patients and 14 controls were imaged to produce normal-appearing white and grey matter T1 histograms. These were used to assess whether histogram measures from principal component analysis (PCA) and linear discriminant analysis (LDA) out-perform traditional histogram metrics in classification of T1 histograms into control and RRMS subject groups and in correlation with the expanded disability status score (EDSS). The histograms were classified into one of two groups using a leave-one-out analysis. In addition, the patients were scanned serially, and the calculated parameters correlated with the EDSS. The classification results showed that the more complex techniques were at least as good at classifying the subjects as histogram mean, peak height and peak location, with PCA/LDA having success rates of 76% for white matter and 68%/65% for grey matter. No significant correlations were found with EDSS for any histogram parameter. These ...

Abstract: This paper presents an automated medicinal plant leaf identification system. The Colour Texture analysis of the leaves is done using the statistical, the Grey Tone Spatial Dependency Matrix(GTSDM) and the Local Binary Pattern(LBP) based features with 20 different colour spaces(RGB, XYZ, CMY, YIQ, YUV, $YC_{b}C_{r}$, YES, $U^{*}V^{*}W^{*}$, $L^{*}a^{*}b^{*}$, $L^{*}u^{*}v^{*}$, lms, $l\alpha\beta$, $I_{1} I_{2} I_{3}$, HSV, HSI, IHLS, IHS, TSL, LSLM and KLT). Classification of the medicinal plant is carried out with 70\% of the dataset in training set and 30\% in the test set. The classification performance is analysed with Stochastic Gradient Descent(SGD), kNearest Neighbour(kNN), Support Vector Machines based on Radial basis function kernel(SVM-RBF), Linear Discriminant Analysis(LDA) and Quadratic Discriminant Analysis(QDA) classifiers. Results of classification on a dataset of 250 leaf images belonging to five different species of plants show the identification rate of 98.7 \%. The ...

Video fire detection is playing an increasingly important role in our life. But recent research is often based on a traditional RGB color model used to analyze the flame, which may be not the optimal color space for fire recognition. It is worse when we research smoke simply using gray images instead of color ones. We clarify the importance of color information for fire detection. We present a fire discriminant color (FDC) model for flame or smoke recognition based on color images. The FDC models aim to unify fire color image representation and fire recognition task into one framework. With the definition of between-class scatter matrices and within-class scatter matrices of Fisher linear discriminant, the proposed models seek to obtain one color-space-transform matrix and a discriminate projection basis vector by maximizing the ratio of these two scatter matrices. First, an iterative basic algorithm is designed to get one-component color space transformed from RGB. Then, a general algorithm is ...

Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation. The methodology that is used in deriving the canonical coefficients parallels that of a one-way multivariate analysis of variance (MANOVA). MANOVA tests for equality of the mean vector across class levels. Canonical discriminant analysis finds linear combinations of the quantitative variables that provide maximal separation between classes or groups. Given a classification variable and several quantitative variables, the CANDISC procedure derives canonical variables, which are linear combinations of the quantitative variables that summarize between-class variation in much the same way that principal components summarize total variation. The CANDISC procedure performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance. Two output data sets can be ...

Serum total cholesterol, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A-I and apolipoprotein B were evaluated as potential indicators of the risk of coronary artery disease in young (less than 46 years) normocholesterolaemic, non-diabetic men who had previously sustained a myocardial infarction (n = 50) and in healthy age and sex matched controls (n = 122) with a similar socioeconomic background. Significant differences were observed between patients and controls in the mean concentrations of serum total cholesterol, triglycerides, low density lipoprotein cholesterol, high density lipoprotein cholesterol and apolipoprotein B, as well as in the ratios of total cholesterol to high density lipoprotein cholesterol and apolipoprotein A-I to apolipoprotein B. No significant difference was demonstrated in the concentration of apolipoprotein A-I between the two groups. Stepwise discriminant analysis indicated that apolipoprotein B was the best ...

A better approach to assess plant genetic diversity is the agro-morphological characterization. The main objective of this study was to investigate the morphological variability of 87 maize (Zea mays L.) accessions collected in different agro-ecological zones of southern Benin. Thus, 16 agro-morphological characters (seven quantitative and nine qualitative) were selected from the maize descriptors. The experimental design used is an incomplete randomized block with three replications. The mixed model analysis of two factors variance revealed a very highly significant difference for all accessions for each quantitative agro-morphological characteristic evaluated. The numerical classification of all corn accessions revealed five groups of accessions. The results of the stepwise discriminant analysis revealed five agro-morphological characteristics (germination days, female flowering, plant height, ear height and sensitivity to streak) most discriminating. The results of numerical classification

The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (CD a). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, CD a). The variable that seems to discriminate better the clusters was the dv/v (F = 53.680; P , .001), followed by the dv (F = 28.506; P , .001), CD a (F = 21.025; P , .001), S (F = 6.297; P , .01) and v (F = 5.375; P = .01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined ...

The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (CD a). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, CD a). The variable that seems to discriminate better the clusters was the dv/v (F = 53.680; P , .001), followed by the dv (F = 28.506; P , .001), CD a (F = 21.025; P , .001), S (F = 6.297; P , .01) and v (F = 5.375; P = .01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined ...

Exercise has been linked to a reduced cancer risk in animal models. However, the underlying mechanisms are unclear. This study assessed the effect of exercise with dietary consideration on the phospholipid profile in 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced mouse skin tissues. CD-1 mice were randomly assigned to one of the three groups: ad libitum-fed sedentary control; ad libitum-fed treadmill exercise at 13.4 m/min for 60 min/d, 5 d/wk (Ex+AL); and treadmill-exercised but pair-fed with the same amount as the control (Ex+PF). After 14 weeks, Ex+PF but not Ex+AL mice showed ∼25% decrease in both body weight and body fat when compared with the controls. Of the total 338 phospholipids determined by electrospray ionization-tandem mass spectrometry, 57 were significantly changed, and 25 species could distinguish effects of exercise and diet treatments in a stepwise discriminant analysis. A 36% to 75% decrease of phosphatidylinositol (PI) levels in Ex+PF mice occurred along with a ...

1] T.W., Anderson, An introduction to Multivariate Statistical Analysis, J. Wiley, New York. , Zbl 0083.14601 [2] J.R. Barra, Notions fondamentales de statistique mathématique, Dunod, Paris, 1971. , MR 402992 , Zbl 0257.62004 [3] M. Okamoto, An asymptotic expansion for the distribution of the linear discriminant function A. M. S., t. 34, 4, 1963, p. 1286-1301. , MR 156419 , Zbl 0117.37101 [4] V. Brailovsky. On influence of sample set structures on Decision Rule. Quality for the case of linear Discriminant Function (à paraître). , Zbl 0504.62053 [5] J.R. Barra, Influence de lestimation des paramètres sur la probabilité derreur en Analyse Discriminante (selon V. Brailovsky), 1979. Séminaire de lÉcole Polytechnique, novembre 1979. ...

I agree with Soeren that you probably have, within your set of possible features, the means to construct a model for identifying real users (or crawlers if thats your intent). I flagged this as a question for CrossValidated the sister site for statistics and machine learning because the community there can provide you with detailed answers to questions like these. Unfortunately that flag was not yet accepted and cross posting is discouraged so Ill try to answer here.. If you were interested to try LDA I would consider QDA instead. Quadratic Discriminant Analysis is LDA without the assumption of both classes having the same variance. You had stated that humans have more randomness and different variances would better fit that assumption.. However you are calling this anomaly detection which is a type of problem neither QDA or LDA might best suited for. Usually this term implies that you feel that the majority class is identifiable but the other class(s) are either rare, or overly heterogenous ...

This presentation will examine the calculation of a likelihood ratio to assess the evidentiary value of fire debris analysis results. Models based on support vector machine (SVM), linear and quadratic discriminant analysis (LDA and QDA ) and k-nearest neighbors (kNN) methods were examined for binary classification of fire debris samples as positive or negative for ignitable liquid residue (ILR). Computational mixing of data from ignitable liquid and substrate pyrolysis databases was used to generate training and cross validation samples. A second validation was performed on fire debris data from large-scale research burns, for which the ground truth (positive or negative for ILR ) was assigned by an analyst with access to the gas chromatography-mass spectrometry data for the ignitable liquid used in the burn. The probabilities of class membership were calculated using an uninformative prior and a likelihood ratio was calculated from the resulting class membership probabilities . The SVM method ...

For two-class classification, it is common to classify by setting a threshold on class probability estimates, where the threshold is determined by {ROC} curve analysis. An analog for multi-class classification is learning a new class partitioning of the multiclass probability simplex to minimize empirical misclassification costs. We analyze the interplay between systematic errors in the class probability estimates and cost matrices for multi-class classification. We explore the effect on the class partitioning of five different transformations of the cost matrix. Experiments on benchmark datasets with naive Bayes and quadratic discriminant analysis show the effectiveness of learning a new partition matrix compared to previously proposed methods.

We propose a steganalytic algorithm for triangle meshes, based on the supervised training of a classifier by discriminative feature vectors. After a normalization step, the triangle mesh is calibrated by one step of Laplacian smoothing and then a feature vector is computed, encoding geometric information corresponding to vertices, edges and faces. For a given steganographic or watermarking algorithm, we create a training set containing unmarked meshes and meshes marked by that algorithm, and train a classifier using Quadratic Discriminant Analysis. The performance of the proposed method was evaluated on six well-known watermarking/steganographic schemes with satisfactory accuracy rates. ...

Summary: The volatile fatty acids produced in culture medium by 357 Pseudomonas strains belonging to eight species were determined quantitatively by GLC. The resultant chromatograms were submitted to discriminant analysis. Stable discriminant functions were computed and included in a computerized identification system which also involved some distinctive volatile fatty acids regarded as two-state qualitative characters (presence or absence characters). Using a test group of 249 strains belonging to the studied species, more than 89% of the identifications made by this system agreed with those made by conventional biochemical methods despite the relatively poor differentiation between P. putida and P. fluorescens. When the individual species within the matrices were weighted with prior probabilities reflecting results given by two simple biochemical tests, 96% of the 249 strains were correctly identified.

The primary objective of this study was to search for proteins or peptides that are differentially expressed between healthy subjects and those with the dry-eye syndrome. Samples were prepared, and then the raw spectral data were acquired and processed as described earlier. Cluster lists were generated for each condition (low- and high-energy laser settings) and for each surface (CM10, H50, and Q10) using the Biomarker Wizard (Ciphergen Biosystems, Inc.). For each of these cluster lists, a multivariate discriminant analysis was performed which defined a total of approximately 50 peaks with significant differences between the tear protein profiles of CTRL and DRY (P , 0.01). The objective of protein profiling is to distinguish healthy from disease states based on protein patterns. In previous studies using electrophoretic separations with subsequent digital image analysis and multivariate statistics, we were able to demonstrate that the tear protein patterns of patients with dry eye differ ...

Jiaqi Ganxian Granule (JGG) is a famous traditional Chinese medicine, which has been long used in clinical practice for treating liver fibrosis. However, the mechanism underlying its anti-hepatic fibrosis is still not clear. In this study, an Ultra-Performance Liquid Chromatography-Time-Of-Flight Mass Spectrometry (UPLC-TOF-MS)-based metabolomics strategy was used to profile the metabolic characteristic of serum obtained from a carbon tetrachloride (CCl4)-induced hepatic fibrosis model in Sprague-Dawley (SD) rats with JGG treatment. Through Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA), it was shown that metabolic perturbations induced by CCl4 were inhibited after treatment of JGG, for 17 different metabolites related to CCl4. Among these compounds, the change tendency of eight potential drug targets was restored after the intervention with JGG. The current study indicates that JGG has a significant anti-fibrosis effect on CCl4-induced liver fibrosis in rats,

Serum and urine samples were collected from 27 patients with dry and 75 with wet macular degeneration. Serum samples were centrifuged to remove cells, and 0.5ml aliquots stored at -80 degree C. After thawing, serum was filtered through 3kD MW cutoff filter to remove proteins. The filtrate was made with 10% in D2O, 100mM phosphate 0.5mM TMSP and pH 7.00.One-dimensional 1H spectra were acquired using a standard spin-echo pulse sequence on a Bruker DRX 600MHz NMR spectrometer equipped with a 1.7mm cryoprobe. 2D JRes spectra were also acquired to aid metabolite identification. Spectra were be segmented into 0.005-ppm (2.5 Hz) chemical shift bins between 0.2 and 10.0 ppm, and the spectral area within each bin integrated. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of the processed data was conducted using PLS Toolbox (Eigenvector Research) within MATLAB ...

In this example, PROC DISCRIM uses normal-theory methods to classify the iris data used in Example 25.1. The POOL=TEST option tests the homogeneity of the within-group covariance matrices (Output 25.3.3). Since the resulting test statistic is significant at the 0.10 level, the within-group covariance matrices are used to derive the quadratic discriminant criterion. The WCOV and PCOV options display the within-group covariance matrices and the pooled covariance matrix (Output 25.3.2). The DISTANCE option displays squared distances between classes (Output 25.3.4). The ANOVA and MANOVA options test the hypothesis that the class means are equal, using univariate statistics and multivariate statistics; all statistics are significant at the 0.0001 level (Output 25.3.5). The LISTERR option lists the misclassified observations under resubstitution (Output 25.3.6). The CROSSLISTERR option lists the observations that are misclassified under cross validation and displays cross validation error-rate ...

Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450-1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression ...

A metabonomics method using 1H nuclear magnetic resonance spectroscopy (1HNMR) was applied to obtain a systematic view of the development and progression of postmenopausal osteoporosis. Using partial least squares discriminant analysis (PLS-DA), 26 and 34 characteristic resonances were found respectively in urine and plasma of ovariectomized rats (Variable importance, VIP value ≥1.0), and the significant altered metabolites identified in the plasma and urine were 10 and 9, respectively. Changes in these metabolites were related to the pathways of lipid, energy and amino acid metabolism, some of which involved the oxidative system. The described method was also used to analyze the therapeutic effects of Er-Xian Decoction (EXD), a traditional Chinese medicine widely used in the clinical treatment of osteoporosis in China. The results showed that EXD administration could provide satisfactory effects on osteoporosis through partially regulating the perturbed pathways of lipid, energy and amino acid

Skeletal trauma analysis of motor vehicle collisions has the potential to support or contradict reported collision circumstances. This project analyzed the skeletal injuries that pedestrians sustain in fatal collisions according to vehicle types (car, truck, SUV, van, bus, semi, etc.). Data were collected from reports and databases related to cases that occurred in King County, Washington. The pelvis and lower extremities of the body were analyzed for the frequency of skeletal fractures, grouped by pelvis, femora, patellae, tibiae, and fibulae skeletal groups. A Kruskal-Wallis test showed an overall no significant difference (P|0.05) in fracture quantity in skeletal regions between different vehicle groups. A multiple pairwise comparison using Dunns procedure also found no significant differences between vehicle type groups. A Partial Least Squares Discriminant Analysis showed an overall success rate of 37.29% when classifying injury profiles to vehicle type. The findings of this project can be applied

Abstract: This study monitored structural shifts of gut microbiota of rats developing precancerous mucosal lesions induced by carcinogen 1,2-dimethyl hydrazine (DMH) treatment using PCR-denaturing gradient gel electrophoresis (DGGE) and 454 pyrosequencing on the 16S rRNA gene V3 region. Partial least square discriminant analysis of DGGE fingerprints showed that the gut microbiota structure of treated animals was similar to that of the controls 1 and 3 weeks after DMH treatments, but significantly different 7 weeks after DMH treatments, when a large number of aberrant crypt foci (ACF) developed in their colons. Martens uncertainty test, followed by anova test ( ...

The purpose of this empirical study conducted to determine the factors influencing student academic performance in Perbanas Institute Jakarta. Multiple Discriminant Analysis and Regression Logistic...

discrim lda income, group(cases) Linear discriminant analysis Resubstitution classification summary +---------+ , Key , ,---------, , Number , , Percent , +---------+ , Classified True cases , normal depressed , Total -------------+----------------------+---------- normal , 121 123 , 244 , 49.59 50.41 , 100.00 , , depressed , 19 31 , 50 , 38.00 62.00 , 100.00 -------------+----------------------+---------- Total , 140 154 , 294 , 47.62 52.38 , 100.00 , , Priors , 0.5000 0.5000 , estat classfunction Classification functions , cases , normal depressed -------------+---------------------- income , .09481 .0664835 _cons , -1.027561 -.5052746 -------------+---------------------- Priors , .5 .5 estat canontest Canonical linear discriminant analysis , , Like- , Canon. Eigen- Variance , lihood Fcn , Corr. value Prop. Cumul. , Ratio F df1 df2 Prob>F ----+---------------------------------+------------------------------------ 1 , 0.1594 .02607 1.0000 1.0000 , 0.9746 7.6125 1 292 0.0062 e ...

Análisis Discriminante Lineal (ADL) es una generalización del discriminante lineal de Fisher, un método utilizado en estadística, reconocimiento de patrones y aprendizaje de máquinas para encontrar una combinación lineal de rasgos que caracterizan o separan dos o más clases de objetos o eventos. La combinación resultante puede ser utilizada como un clasificador lineal, o, más comúnmente, para la reducción de dimensiones antes de la posterior clasificación. LDA está estrechamente relacionado con el análisis de varianza (ANOVA) y el análisis de regresión, el cual también intenta expresar una variable dependiente como la combinación lineal de otras características o medidas. Sin embargo, ANOVA usa variables independientes categóricas y una variable dependiente continua, mientras que el análisis discriminante tiene variables independientes continuas y una variable dependiente categórica (o sea, la etiqueta de clase). La regresión logística y la regresión probit son más ...

OPUS (Open Publications of UTS Scholars) is the UTS institutional repository. It showcases the research of UTS staff and postgraduate students to a global audience. For you, as a researcher, OPUS increases the visibility and accessibility of your research by making it openly available regardless of where you choose to publish.. Items in OPUS are enhanced with high quality metadata and seeded to search engines such as Google Scholar as well as being linked to your UTS research profile, increasing discoverability and opportunities for citation of your work and collaboration. In addition, works in OPUS are preserved for long-term access and discovery.. The UTS Open Access Policy requires UTS research outputs to be openly available via OPUS. Depositing your work in OPUS also assists you in complying with ARC, NHMRC and other funder Open Access policies. Providing Open Access to your research outputs through OPUS not only ensures you comply with these important policies, but increases opportunities ...

TY - JOUR. T1 - Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset. AU - Tsukuda, Koji. AU - Mano, Shuhei. AU - Yamamoto, Toshimichi. PY - 2020. Y1 - 2020. N2 - Short Tandem Repeats (STRs) are a type of DNA polymorphism. This study considers discriminant analysis to determine the population of test individuals using an STR database containing the lengths of STRs observed at more than one locus. The discriminant method based on the Bayes factor is discussed and an improved method is proposed. The main issues are to develop a method that is relatively robust to sample size imbalance, identify a procedure to select loci, and treat the parameter in the prior distribution. A previous study achieved a classification accuracy of 0.748 for the g-mean (geometric mean of classification accuracies for two populations) and 0.867 for the AUC (area under the receiver operating characteristic curve). We improve the maximum values for the g-mean ...

Galvez and colleagues have demonstrated in several publications that the SIR descriptors can be used effectively in discriminant analysis.224647 In addition, they have shown that screening a structure library found compounds that proved to be active.. A database with compounds in seven different pharmacological classes of activity was used for development of discriminant models of each activity class. The classes included analgesic, antiviral, bronchodilator, antifungal, hypolipidemic, hypoglycemic, and beta-blocking activity. Galvez developed separate discriminant models for each class by using molecular connectivity indices. Based on each model, activity was predicted for a list of structures as both a prediction and a subsequent experimental test. In some cases, compounds were also tested experimentally. Compounds predicted to be active were generally known from the literature to be active or tested in the laboratory.. For example, for antiviral activity, a library of over 12000 commercial ...

0031] Since the adaptive learning is a greedy method, it needs a good starting point to converge to a good solution. The starting point can be the discriminant learned offline from collected training samples, denoted as A.sup.(0). Even if the initial discriminant does not fit the current environment, the adaptive learning can quickly converge to a good solution. FIG. 4 illustrates an example of adaptive discriminant learning for detection of a pigtail catheter in a fluoroscopic image sequence. In the example of FIG. 4, the pigtail catheter appears as almost a line and the initial discriminant model has a large error of above 40%. During tracking, A is updated at each frame based on the tracked results (or the initialization at the first frame) as positive samples and image patches away from the tracked objects as negative samples. Image (a) of FIG. 4 shows positive samples 402 and negative samples 404 extracted from a frame of a fluoroscopic image sequence. In a possible implementation, the ...

For all classifications, applying a threshold of 80% to the eANN outputs increases median correct classification rate across the remaining species/ groups to 95%. Further increasing the threshold does not significantly increase correct classification rate. We therefore recommend applying a threshold of 80% to the eANN outputs, so that calls with eANN outputs below 80% are left unclassified at that level of the hierarchy. Classification should then be assigned by the last stage of the hierarchy for which the output is greater than 80%. ...

The statistical treatment of geochemical data from sediments overlying the Whiteside Granite and the Tallulah Falls Formation suggest that the soils associated with these lithologies can be delineated on the bases of Cu, Mn, Sn, U, and Zn. That is, the bedrock units exhibit a unique geochemical fingerprint defined by these five parameters. Similarly, materials from differing sediment sources within the Whiteside Granite, including forests, roads, lawns, and alluvial deposits along upland streams can be defined on the basis of Ag, Bi, Cr, Mn, Mo, Ni, Sb, Sn, and Zn. Thus, the results from the linear discriminant analysis suggest that it is possible to use sediment mixing models to determine the quantity of material derived from differing lithologies or land-cover types. In light of the above, a separate sediment mixing model was developed using the parameters defined in the discriminant analysis to (1) assess the relative contributions of sediment derived from the different bedrock units that ...

TY - JOUR. T1 - A comparison of resampling schemes for estimating model observer performance with small ensembles. AU - Elshahaby, Fatma E.A.. AU - Jha, Abhinav Kumar. AU - Ghaly, Mickel. AU - Frey, Eric. PY - 2017/8/22. Y1 - 2017/8/22. N2 - In objective assessment of image quality, an ensemble of images is used to compute the 1st and 2nd order statistics of the data. Often, only a finite number of images is available, leading to the issue of statistical variability in numerical observer performance. Resampling-based strategies can help overcome this issue. In this paper, we compared different combinations of resampling schemes (the leave-one-out (LOO) and the half-train/half-test (HT/HT)) and model observers (the conventional channelized Hotelling observer (CHO), channelized linear discriminant (CLD) and channelized quadratic discriminant). Observer performance was quantified by the area under the ROC curve (AUC). For a binary classification task and for each observer, the AUC value for an ...

Linear Discriminant Analysis (LDA) In LR, we estimate the posterior probability directly. In LDA we estimate likelihood and then use Bayes theorem. Calculating posterior using bayes theorem is easy in case of classification because hypothesis space is limited. Equation 4 is derived from equation 3 only. Probability(k) would be highest for the class for which…

When you specify METHOD=NORMAL to derive a linear or quadratic discriminant function, you can save the calibration information developed by the DISCRIM procedure in a SAS data set by using the OUTSTAT= option in the procedure. PROC DISCRIM then creates a specially structured SAS data set of TYPE=LINEAR, TYPE=QUAD, or TYPE=MIXED that contains the calibration information. For more information about these data sets, see Appendix A: Special SAS Data Sets. Calibration information cannot be saved when METHOD=NPAR, but you can classify a TESTDATA= data set in the same step. For an example of this, see Example 35.1. To use this calibration information to classify observations in another data set, specify both of the following: ...

Infrared spectra photometric data utilized here provided a means of comparison between the compositional data set on the one hand and the infrared data set on the other. The plot of canonical variates of the ratio of infrared absorbance also classified the different test samples. The extent of this classification is reduced by the use of infrared absorbances. The less distinctive nature of the classification model could be attributed to the constraint posed by instrumentation since the infrared absorbance at the near infrared region was not measured here in the analytical step. At the near infrared region, the measured absorbances give stronger indication of the characterization factors of the crude samples and if incorporated into the discriminant test would probably enhance the discriminatory capabilities of these fingerprints. However, the same obvious pattern was observed in the classification model as in the compound type module. The crude samples which were classified tended to be ...

List of words make out of Discriminants. Anagrams and Words made out of Discriminants. Find Scrabble Point of Discriminants. Definition of Discriminants. Puzzle Solver.

The impact on beef herd performance of weaning calves before or after cows clean-up low quality summer pasture was assessed. Surplus pasture was cleaned-up by weaned cows, and unweaned cows and calves, using either a single hard grazing (1 x 56 d rotation) or 2 x 28 d rotations commencing 20 January in 1986 and 22 January in 1987. Pastures had accumulated about 4.5 t dry matter (DM) /ha after being retired from grazing in late November as a pasture control measure. Weaned calves were fed quality (70+% green) pasture at pre-graze masses of 1.5, 2.0 or 2.3 t DM/ha under set-stocking 1986, or a fast rotation, 1987. Unweaned calves gained 38 and 39 kg over the 8 weeks in 1986 and 1987, respectively. There was no effect of pasture clean-up method on calf growth. Calves in the best weaned treatment gained just 23 and 30 kg in 1986 and 1987, respectively. Weaned calves suffered from facial eczema (mean gamma glutamyl transferase )GGT) 290 iu) in 1986 which reduced their growth. The rank pasture reduced ...

Discriminant Analysis (DA) is widely applied in many fields. Some recent researches raise the fact that standard DA assumptions, such as a normal distribution of data and equality of the variance-covariance matrices, are not always satisfied. A Mathematical Programming approach (MP) has been frequently used in DA and can be considered a valuable alternative to the classical models of DA. The MP approach provides more flexibility for the process of analysis. The aim of this paper is to address a comparative study in which we analyze the performance of three statistical and some MP methods using linear and nonlinear discriminant functions in two-group classification problems. New classification procedures will be adapted to context of nonlinear discriminant functions. Different applications are used to compare these methods including the Support Vector Machines- (SVMs-) based approach. The findings of this study will be useful in assisting decision-makers to choose the most appropriate model for their

Suppose optimal number of clusters come out 4. We need to check whether or not the clusters overlap with each other in terms of their location in the k-dimensional space 14 variables. It is not possible to visualize clusters in 14 dimensions. To work around this problem, we can use canonical discriminant analysis which is a data reduction technique that creates a smaller number of variables that are linear combinations of the 14 clustering variables. The new variables called canonical variables are ordered in terms of the proportion of variance in the clustering variable that is accounted for by each of the canonical variables. So the first canonical variable will account for the largest proportion of the variance ...

To understand which instruments and symptoms best discriminate episodes, canonical discriminant analyses (CDAs) were conducted. These statistical procedures find patterns of canonical correlation between features that separate scores and items according to a dependent variable. In other words, CDA is a type of regression that allows identification of which items or instruments are better than others to separate groups. Three indexes are used to interpret CDA: chi-square, Wilks lambda, and the standardized canonical coefficient (SCC). The chi-square statistic reveals whether the variable is able to discriminate groups in a significant manner (p < 0.05). Wilks lambda tests the extent to which a variable contributes to discrimination: the closer to 0 the index, the higher the extent to which the variable contributes to separate groups. Finally, the SCC ranks the importance of variables to separate groups; i.e., the higher the coefficient, the more important the variable. SCC and Wilks lambda are ...

In this work the effects of simple imputations are studied, regarding the integration of multimodal data originating from different patients. Two separate datasets of cutaneous melanoma are used, an image analysis (dermoscopy) dataset together with a transcriptomic one, specifically DNA microarrays. Each modality is related to a different set of patients, and four imputation methods are employed to the formation of a unified, integrative dataset. The application of backward selection together with ensemble classifiers (random forests), followed by principal components analysis and linear discriminant analysis, illustrates the implication of the imputations on feature selection and dimensionality reduction methods. The results suggest that the expansion of the feature space through the data integration, achieved by the exploitation of imputation schemes in general, aids the classification task, imparting stability as regards the derivation of putative classifiers. In particular, although the ...

TY - GEN. T1 - Detecting EEG evoked responses for target image search with mixed effect models. AU - Huang, Yonghong. AU - Erdogmus, Deniz. AU - Mathan, Santosh. AU - Pavel, Misha. PY - 2008. Y1 - 2008. N2 - There is evidence that brain signals associated with perceptual processes can be used for target image search. We describe the application of mixed effect models (MEMs) to brain signature detection. We develop an MEM detector for detecting brain evoked responses generated by perceptual processes in the human brain associated with detecting novel target stimuli. We construct the model using principal component analysis and linear discriminant analysis (LDA) bases. We adopt the LDA for dimension reduction. For parameter regularization we use 10-fold cross validation and report experimental results from six subjects. Four out of six subjects achieve very good detection performance with more than 0.9 areas under receiver operating characteristic curves. The results demonstrate that the MEM can ...

TY - JOUR. T1 - Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data. AU - Yourganov, Grigori. AU - Chen, Xu. AU - Lukic, Ana S.. AU - Grady, Cheryl L.. AU - Small, Steven L.. AU - Wernick, Miles N.. AU - Strother, Stephen C.. PY - 2011/5/15. Y1 - 2011/5/15. N2 - Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of ...

Supervised Classification Using Sparse Fishers LDA. Irina Gaynanova, James G. Booth, Martin T. Wells(Submitted on 21 Jan 2013 (v1), last revised 16 Sep 2014 (this version, v2)). It is well known that in a supervised classification setting when the number of features is smaller than the number of observations, Fishers linear discriminant rule is asymptotically Bayes. However, there are numerous modern applications where classification is needed in the high-dimensional setting. Naive implementation of Fishers rule in this case fails to provide good results because the sample covariance matrix is singular. Moreover, by constructing a classifier that relies on all features the interpretation of the results is challenging. Our goal is to provide robust classification that relies only on a small subset of important features and accounts for the underlying correlation structure. We apply a lasso-type penalty to the discriminant vector to ensure sparsity of the solution and use a shrinkage type ...

Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlatio