文件列表:

Linear Discriminant Analysis (LDA)\DF_LDA_TNN.pdf (483064, 2004-11-01)
Linear Discriminant Analysis (LDA)\F_JDLDAPro.m (4990, 2004-11-02)
Linear Discriminant Analysis (LDA)\R_LDA_PRL.pdf (335890, 2004-10-29)

********************************************************************************* * Matlab source codes for the regularized linear discriminant analysis (R-LDA) * * Author: Lu Juwei * * Bell Canada Multimedia Lab, Dept. of ECE, U. of Toronto * * Released in 01 November 2004 * ********************************************************************************* The matlab functions implement the methods presented in the paper [DF_LDA_TNN.pdf] Juwei Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Face Recognition Using LDA Based Algorithms", IEEE Transactions on Neural Networks, Vol. 14, No. 1, Page: 195-200, January 2003. and the paper [R_LDA_PRL.pdf] (An extension to the above TNN paper) Juwei Lu, K.N. Plataniotis, A.N. Venetsanopoulos, “Regularization Studies of Linear Discriminant Analysis in Small Sample Size Scenarios with Application to Face Recognition”, Pattern Recognition Letter, vol. 26, issue 2, pp. 181-191, 2005. [Usages:] 1. To find the KDDA based feature representation with RBF kernel, 1.1. use function [DFLD_Trans]=F_JDLDAPro() to find the regularized LDA subspace. 1.2. given a sample, x, its projection (y) to the above subspace can be obtained by y=DFLD_Trans*x. [Note:] The regularization parameter $eta$ in the function F_JDLDAPro() has an influence on the classification performance. Try different values of $eta$ to find the best one. [Restrictions:] In all documents and papers that report on research that uses the matlab codes, the researcher(s) must reference the following two papers: [1] Juwei Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Face Recognition Using LDA Based Algorithms", IEEE Transactions on Neural Networks, Vol. 14, No. 1, Page: 195-200, January 2003. [2] Juwei Lu, K.N. Plataniotis, A.N. Venetsanopoulos, “Regularization Studies of Linear Discriminant Analysis in Small Sample Size Scenarios with Application to Face Recognition”, Pattern Recognition Letter, vol. 26, issue 2, pp. 181-191, 2005. Any comments and questions can be sent to juwei@dsp.utoronto.ca.

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