文件列表:

r-kdda（规范化参数的KDDA）\F_EigenSys.m (369, 2003-09-03)
r-kdda（规范化参数的KDDA）\F_KDDA_PolyPrj.m (1196, 2003-09-03)
r-kdda（规范化参数的KDDA）\F_KDDA_PolyPro.m (5408, 2004-11-01)
r-kdda（规范化参数的KDDA）\F_KDDA_RbfPrj.m (1175, 2004-05-25)
r-kdda（规范化参数的KDDA）\F_KDDA_RbfPro.m (5386, 2004-11-01)
r-kdda（规范化参数的KDDA）\JLu_KP_ANV.pdf (547786, 2004-10-04)
r-kdda（规范化参数的KDDA）\TNN_KDDA02.pdf (270078, 2004-10-14)
r-kdda（规范化参数的KDDA） (0, 2007-06-11)

************************************************************************** * Matlab source codes for the kernel direct discriminant analysis (KDDA) * * Author: Lu Juwei * * Bell Canada Multimedia Lab, Dept. of ECE, U. of Toronto * * Released in 03 September 2003 * ************************************************************************** The matlab functions implement the methods presented in the paper [TNN_KDDA02.pdf] Juwei Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms", IEEE Transactions on Neural Networks, Vol. 14, No. 1, Page: 117-126, January 2003. and the chapter [JLu_KP_ANV.pdf] (An extension to the above TNN paper) Juwei Lu, K.N. Plataniotis and A.N. Venetsanopoulos, “Kernel Discriminant Learning with Application to Face Recognition”, to appear, in “Support Vector Machines: Theory and Applications”, Lipo WANG, Editors, Springer-Verlag, to be published in 2004. [Usages:] 1. To find the KDDA based feature representation with RBF kernel, 1.1. use function F_KDDA_RbfPro() to find the kernel discriminant subspace. 1.2. use function F_KDDA_RbfPrj() to project the test samples into the kernel discriminant subspace. 2. To find the KDDA based feature representation with polynomial kernel, 2.1. use function F_KDDA_PolyPro() to find the kernel discriminant subspace. 2.2. use function F_KDDA_PolyPrj() to project the test samples into the kernel discriminant subspace. [Note:] In addition to the kernel function and its involved parameters, the regularization parameter $eta$ in function F_KDDA_Rbf()/F_KDDA_Poly() does affect the classification performance. Try different values of these parameters 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 paper: Juwei Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms", IEEE Transactions on Neural Networks, Vol. 14, No. 1, Page: 117-126, January 2003. Any comments and questions can be sent to juwei@dsp.utoronto.ca.

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