Topic
Linear discriminant analysis
About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.
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TL;DR: An experiment to help identify the relevant higher order features of texture perceived by humans using the techniques of hierarchical cluster analysis, non-parametric multidimensional scaling (MDS), Classification and Regression Tree Analysis (CART), discriminant analysis, and principal component analysis.
223 citations
01 Jan 1983
TL;DR: Using the same basic framework, a procedure is proposed to test the structural similarity of two distributions, and the form which results provides a unified view of the parametric nearest mean reclassification algorithm and the nonparametric valley seeking algorithm.
223 citations
27 Aug 2007
TL;DR: In this paper, a discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition.
Abstract: A novel discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition The face image is first partitioned into several non-overlapping regions In each region, multi-scale local binary uniform pattern histograms1 are extracted and concatenated into a regional feature The features are then projected on the LDA space to be used as a discriminative facial descriptor The method is implemented and tested in face identification on the standard Feret database and in face verification on the XM2VTS database with very promising results
222 citations
TL;DR: In this paper, several discriminant and multiple regression analyses were performed on retail credit application data to develop a numerical scoring system for predicting credit risk in a finance company, and the results showed that equal weights for all significantly predictive items were as effective as weights from the more sophisticated techniques of discriminant analysis and stepwise multiple regression.
Abstract: Several discriminant and multiple regression analyses were performed on retail credit application data to develop a numerical scoring system for predicting credit risk in a finance company. Results showed that equal weights for all significantly predictive items were as effective as weights from the more sophisticated techniques of discriminant analysis and “stepwise multiple regression.” However, a variation of the basic discriminant analysis produced a better separation of groups at the lower score levels, where more potential losses could be eliminated with a minimum cost of potentially good accounts.
222 citations
TL;DR: Robust discriminant rules are obtained by inserting robust estimates of location and scatter into generalized maximum likelihood rules at normal distributions and the highly robust MCD estimator is used as it can be computed very fast for large data sets.
221 citations