Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Discriminant Analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources
PubMed1995

Categorization of heart rate-eye movement patterns in human fetuses using the statistical technique of discriminant analysis.

The purpose of this study was to determine if discriminant analysis could be used to categorize fetal heart rate (FHR) - fetal eye movement (FEM) patterns. Statistical characteristics from 27 normal human fetuses at term for behavioral states, transitions, and insertions were established by combining the digitized FHR-FEM data for subjectively identical epochs. The mean FHR, the variance about the mean, and the presence or absence of FEM were calculated for each 3-min block in a sliding moving window with a 1-min step size. For each fetus, discriminant analysis was then used to assign 3-min blocks to either a behavioral state, a transition, or an insertion by comparing the statistical properties of a 3-min block with that of the data base. We found no difference between discriminant analysis and visual assignment in the average time spent in behavioral states 1F, 2F, and 4F, or in the mean duration of the transition/insertion periods. There was a highly significant linear relationship between computer-generated and visually-determined durations for behavioral states 1F (r = 0.972, p < 0.0001) and 2F (r = 0.989, p < 0.0001) and for the transition/insertion periods (r = 0.863, p < 0.0001). We conclude that discriminant analysis is a reliable computer-based method for behavioral state identification.

Discriminant Analysis↗
PubMed2000

Generalized discriminant analysis using a kernel approach.

We present a new method that we call generalized discriminant analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is close to the support vector machines (SVM) insofar as the GDA method provides a mapping of the input vectors into high-dimensional feature space. In the transformed space, linear properties make it easy to extend and generalize the classical linear discriminant analysis (LDA) to nonlinear discriminant analysis. The formulation is expressed as an eigenvalue problem resolution. Using a different kernel, one can cover a wide class of nonlinearities. For both simulated data and alternate kernels, we give classification results, as well as the shape of the decision function. The results are confirmed using real data to perform seed classification.

Algorithms↗
PubMed2006

Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.

A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.

Data Interpretation, Statistical↗
PubMed2004

An optimization criterion for generalized discriminant analysis on undersampled problems.

An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative sizes of the data dimension and sample size, overcoming a limitation of classical LDA. The optimization problem can be solved analytically by applying the Generalized Singular Value Decomposition (GSVD) technique. The pseudoinverse has been suggested and used for undersampled problems in the past, where the data dimension exceeds the number of data points. The criterion proposed in this paper provides a theoretical justification for this procedure. An approximation algorithm for the GSVD-based approach is also presented. It reduces the computational complexity by finding subclusters of each cluster and uses their centroids to capture the structure of each cluster. This reduced problem yields much smaller matrices to which the GSVD can be applied efficiently. Experiments on text data, with up to 7,000 dimensions, show that the approximation algorithm produces results that are close to those produced by the exact algorithm.

Algorithms↗
PubMed1997

Value of logistic discriminant analysis for interpreting initial visual field defects.

PURPOSE: The authors evaluate logistic discriminant analysis as a method for interpreting visual field results in initial stages of several ophthalmic diseases. METHODS: The authors retrospectively studied the visual field defects of 96 patients with early glaucomatous damage and prospectively studied 84 subjects with normal eyes (n = 28), cataracts (n = 27), diabetic retinopathy (n = 14), or hypertensive retinopathy (n = 15). The visual fields were examined at least twice with program G1 of Octopus 500 (Interzeag AG, Schlieren, Switzerland). Only one eye per patient was randomly selected and included in the study. The authors created a database with all visual field information provided by Octopus and applied logistic discriminant analysis (SAS Logistic Procedures, SAS Institute, Cary, NC) to obtain classification rules capable of qualifying visual field defects as glaucomatous or nonglaucomatous. The rules were tested with an independent sample. RESULTS: There were significant differences between the groups in the distribution of visual field defects tested by program G1. Logistic discriminant analysis correctly identified the glaucomatous or nonglaucomatous origin of the defects with a sensitivity of 65% to 85% and a specificity of 60% to 92%. CONCLUSIONS: Logistic discriminant analysis is a useful tool to aid in the interpretation of early glaucomatous and nonglaucomatous visual field defects.

Data Interpretation, Statistical↗
PubMed1989

Predictive value of discriminant analysis of monocyte ultrastructure in malignant lymphoma.

Discriminant analysis was applied to morphometric data obtained from ultrastructural studies of blood monocytes from 20 normal subjects, 23 patients with Hodgkin's disease and 12 patients with non-Hodgkin's lymphoma. The aim was to assess the efficiency of predicting subject groups from such data. The analysis, performed on a microcomputer using a standard statistical package, considered nuclear volume, nuclear surface area, nucleolar volume, nucleolar surface area, nucleolar volume fraction, number of nucleoli per section, cell surface area, mitochondrial surface area and subject age. The overall agreement between predicted and actual subject groups was 64%; considering only normality and disease, the agreement was 80%. While the predictive value of such data from circulating monocytes would appear insufficient for diagnostic purposes, discriminant analysis as used here might be of value in indicating the state of host defense in malignancy.

Discriminant Analysis↗
PubMed1998

Analysis of quantitative EEG with artificial neural networks and discriminant analysis--a methodological comparison.

Artificial neural networks (ANN) are widely used to solve problems of differentiating between groups. However, serious comparisons of this method with the traditional procedure for such tasks (discriminant analysis) are rare. Discussing the results of both methods with the example of highly topical data, we try to demonstrate advantages and drawbacks of both methods. For this purpose, quantitative EEGs of 78 alcoholics were investigated in order to determine whether it is possible to predict relapse of these patients at the beginning of treatment. ANN software is available in Kassel (Institute for Computer Sciences and Mathematics).

Alcoholism↗
PubMed2005

Second order discriminant analysis in chemopreventive efficacy measurement.

OBJECTIVE: To describe the use of second order discriminant analysis as a classification methodology along with the underlying assumptions and sampling requirements, with special emphasis on the use of this analysis in chemopreventive efficacy studies. STUDY DESIGN: The discriminant function score distributions derived in an analysis of 2 diagnostic groups may show such overlap that a statistically significant difference in mean values cannot be shown and, more important, that a useful case-based classification cannot be attained. By using the discriminant function score distributions from each case, it is frequently possible to derive a second order discriminant function based on case-specific characteristics, rather than characteristics of nuclei, thereby attaining improved case classification. RESULTS: Second order discriminant analysis has proven very useful in the documentation of case-level efficacy in chemopreventive trials. In a study of orally administered vitamin A, a first order discriminant analysis did not achieve a statistically significant difference in the score distributions for nuclei, but a second order discriminant analysis allowed a correct recognition of intervention effects in 85% of submitted cases. In a chemopreventive study of triamcinolone, a similarly inadequate discrimination based on discriminant function scores for nuclei resulted. After a second order discriminant analysis, a reduction in solar-actinic damage could be shown in 14/15, or 93%, of treated cases. CONCLUSION: Second order discriminant analysis can be highly effective when the discriminating information offered at the nuclear level is inadequate due to high dispersion and small differences in mean values of discriminant function scores for the diagnostic groups. Second order analysis utilizes case-specific characteristics of the discriminant function score distributions to document diagnostic group separation and/or efficacy of chemopreventive intervention by a reduction in case discriminant function scores.

Administration, Oral↗
PubMed1991

Significance of levels of circulating IgA-class immune complex in discriminant analysis of patients with IgA nephropathy before renal biopsy.

Discriminant analysis of clinical markers including circulating IgA-class immune complex (IgA-CIC) before renal biopsy in patients with IgA nephropathy is described. Fifty-six patients with IgA nephropathy (IgA nephropathy group) and 54 patients with other primary chronic glomerulonephritis (non-IgA nephropathy group) were examined. Discriminant analysis was applied to separate these two groups by using 21 clinical markers including levels of IgA-CIC. The levels of IgA-CIC in sera were measured by a solid-phase anti-C3 Facb enzyme immunoassay (EIA). Among these clinical markers, the levels of serum IgA, IgA-CIC and creatinine, and the degree of microhematuria in the IgA nephropathy group were significantly higher than those in the non-IgA nephropathy group. Contributions of IgA and IgA-CIC to the classification were very high and both had almost the same effect. The correct classification rate was 80.00% using five clinical markers: serum IgA, microhematuria, IgA-CIC, serum creatinine, and blood urea nitrogen. It was shown that the levels of serum IgA and IgA-CIC were major markers for the clinical diagnosis of patients with IgA nephropathy. It was concluded that discriminant analysis before renal biopsy was useful for the diagnosis of IgA nephropathy.

Adolescent↗
PubMedDate not supplied

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Adult↗
PubMed1996

[Detection of multivessel lesions after myocardial infarction. Improvement of the predictive value of early exercise stress test by discriminant analysis].

The aim of this study was to assess, by a discriminant analysis, the different parameters of exercise stress testing associated with multivessel disease after uncomplicated myocardial infarction and to determine whether their combination improved the diagnostic value of ST depression alone, the usual diagnostic criterion. One hundred and seventeen out of 240 consecutive pts admitted for acute myocardial infarction between october 1992 and may 1994 underwent early exercise stress testing and coronary angiography 8.5 +/- 3 days and 13 +/- 8 days respectively after infarction. The population was divided into two groups: a "study" group (pts recruited between october 1992 and october 1993) for whom a diagnostic equation had been established based on a discriminant analysis, and "a control" group (pts recruited between november 1993 and may 1994) allowing validation of the diagnostic equation. Of the 9 clinical and 14 exercise stress test variables, only 3 remained statistically significant after discriminant analysis in this study group: the number of METS achieved (p < 0.0005), maximal ST depression in V5 (p < 0.005) and maximal heart rate (p < 0.01). Using these three parameters, a discriminating equation was established in the study group and then validated in the control group. Using this equation, the percentage of pts correctly identified as having multivessel disease was 75% in the study group and 79% in the control group, whereas ST depression, the most commonly used criterion, only classified 68% of the study group and 60% of the control group correctly. This study confirmed the good tolerance of early maximal exercise stress testing after uncomplicated myo-cardial infarction. The combination of three easily discernable parameters improved the diagnostic performance of the stress test in identifying multivessel disease after myocardial infarction.

Adult↗
PubMed2006

Regularized linear discriminant analysis and its application in microarrays.

In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.

Computer Simulation↗
PubMed2001

Analysis of MTR histograms in multiple sclerosis using principal components and multiple discriminant analysis.

Magnetization transfer ratio (MTR) histograms have the potential to characterize subtle diffuse changes in multiple sclerosis (MS) and other white matter disease. A new method is described which gives improved correlation with the Expanded Disability Status Scale (EDSS). Classification of individual subjects into normal and MS subgroups is shown. Principal component analysis (PCA) and multiple discriminant analysis (MDA) are shown to give results superior to methods of MTR histogram analysis using traditional features such as peak height and peak location. Scatterplots confirm the improved separation between groups achieved using the MDA score. The histogram analysis provides a comparison of two classification approaches, based on PCA and MDA, to recognize differences between normal controls and the four different subgroups of MS disease (and all MS patients). Multiple linear regression of these PCs vs. EDSS established an MR-based measure of disease. Using a central 60-mm slab of brain tissue, the success rate of binary classification between control and MS subgroups using MDA was 75-95%, depending on which two groups were being compared. Multiple regression analysis of EDSS with the first three PCs as independent variables was significant (r = 0.83 for secondary progressive MS, and r = 0.80 for all MS patients).

Brain↗
PubMed2004

Application of discriminant analysis and quantitative cytologic examination to gastric lesions.

OBJECTIVE: To investigate of the potential value of morphometry and discriminant analysis for the classification of benign and malignant gastric cells and lesions. STUDY DESIGN: The data set consisted of 13,300 cells from 120 cases composed of 30 cases of cancer, 26 cases of gastritis and 64 cases of ulcer according to the final histologic diagnosis. The cytologic diagnosis was divided into 5 categories (gastritis, ulcer, inflammatory dysplasia, cancer and true dysplasia). Classification was attempted at 2 levels: the cell level to classify individual cells and the case level to classify individual cases. For the cellular classification the measured cells from 50% of available cases were selected as a training set to construct a model. The cells from the remaining cases were used as a test set to validate the model. Similarly for case classification, the same 50% of cases that were used for cell classification were used as a training set and the remaining cases as a test set. Images of routinely processed gastric smears stained by the Papanicolaou technique were analyzed by a customized image analysis system. RESULTS: Application of discriminant analysis on the test set gave correct classification of 98.4% of benign cells and 67.1% of malignant cells. On case classification, 100% accuracy was achieved for benign and malignant cases, both for the training and test sets. CONCLUSION: The application of discriminant analysis described in this paper could produce significant classification results at the cellular and individual case level.

Cell Size↗
PubMed2005

Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image.

We present a novel method of nonlinear discriminant analysis involving a set of locally linear transformations called "Locally Linear Discriminant Analysis (LLDA)." The underlying idea is that global nonlinear data structures are locally linear and local structures can be linearly aligned. Input vectors are projected into each local feature space by linear transformations found to yield locally linearly transformed classes that maximize the between-class covariance while minimizing the within-class covariance. In face recognition, linear discriminant analysis (LDA) has been widely adopted owing to its efficiency, but it does not capture nonlinear manifolds of faces which exhibit pose variations. Conventional nonlinear classification methods based on kernels such as generalized discriminant analysis (GDA) and support vector machine (SVM) have been developed to overcome the shortcomings of the linear method, but they have the drawback of high computational cost of classification and overfitting. Our method is for multiclass nonlinear discrimination and it is computationally highly efficient as compared to GDA. The method does not suffer from overfitting by virtue of the linear base structure of the solution. A novel gradient-based learning algorithm is proposed for finding the optimal set of local linear bases. The optimization does not exhibit a local-maxima problem. The transformation functions facilitate robust face recognition in a low-dimensional subspace, under pose variations, using a single model image. The classification results are given for both synthetic and real face data.

Algorithms↗
PubMed2006

Prognostic model of stage II non-small cell lung cancer by a discriminant analysis of the immunohistochemical protein expression.

PURPOSE: We aimed to identify the key proteins that influence the prognosis of non-small cell lung cancer (NSCLC) using protein expression profiles of previously known prognostic markers. METHODS: Thirty-one cases of Stage II NSCLC with 5-year follow-up data were selected. Tissue microarrays (TMA) and immunohistochemistry were used to make protein expression profiles of 18 previously reported immunohistochemical prognostic markers and their value in NSCLC was statistically re-evaluated by a discriminant analysis. RESULTS: For the discriminant analysis using marker protein expression profiles, we selected three significant markers, TTF-1, RCAS1 and c-MET, to evaluate each patient's 5-year survival. The requested discriminant function was V = -1.08754 x (RCAS1 score) - 0.83174 x (TTF1 score) + 0.55204 x (cMET score) + 5.46972, and V = 0 served as a cut-off point. The correctness for evaluating a patient's 5-year survival by a discriminant analysis was 87.1%. CONCLUSIONS: A discriminant analysis is thus considered to be a useful statistical method for analyzing the protein expression profiles obtained by combined TMA and immunohistochemical techniques using archival NSCLC tissues. However, the sample size and selection of the marker protein depending on the histology greatly influence the results of a NSCLC study.

Antigens, Neoplasm↗