对传统PCA算法改进的2DPCA算法

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Matlab source codes for % % Multilinear Principal Component Analysis (MPCA) % % % % Author: Haiping LU % % Email : hplu@ieee.org or eehplu@gmail.com % % Affiliation: Department of Electrical and Computer Engineering % % University of Toronto % % Release date: June 24, 2008 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %[Algorithms]% The matlab codes provided here implement two algorithms presented in the paper "MPCA_TNN08Jan.pdf" included in this package: Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008. Algorithm 1: "MPCA.m" implements the MPCA algorithm described in this paper Algorithm 2: "MPCALDA.m" implements the MPCA+LDA algorithm in this paper %[Usages]% Please refer to the comments in the codes, which include example usage on 2D data and 3D data below: FERETC80A45.mat: 320 faces (32x32) of 80 subjects (4 samples per class) from the FERET database USF17Gal.mat: 731 gait samples (32x22x10) of 71 subjects from the gallery set of the USF gait challenge data sets version 1.7 %[Restriction]% In all documents and papers reporting research work that uses the matlab codes provided here, the respective author(s) must reference the following paper: [1] Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008. %[Comment/Question?]% Please send your comment (e.g., ways to improve the codes) or question (e.g., difficulty in using the codes) to hplu@ieee.org or eehplu@gmail.com %[Update history]% 1. March 1, 2008: Version 1.0 is released. 2. June 24, 2008: Version 1.1 is released.