Logistic Discriminant Analysis
Logistic Discriminant Analysis Feature extraction is one of the most important problems in pattern recognition. Linear discriminant analysis (LDA) is one of the well-known methods to extract the best features for multi-class discrimination. LDA is formulated as a problem to find an optimal linear mapping by which the within-class scatter in the mapped feature space is made as small as possible relative to the between-class scatter. LDA is useful for linear separable cases, but for more complicated cases, it is necessary to extend it to non-linear. ---------------------------------------------------------------------------------------------------------------------------- Prerequisite : · Bayesian Decision Theory · Multi-Variate Linear Algebra · Numerical Computations -----------------------------------------...