This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper
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.
%[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
%[Toolbox]% The code needs the tensor toolbox available at http://csmr.ca.sandia.gov/~tgkolda/TensorToolbox/
%[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:
 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.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Linux, Windows, Unix, Solaris
- Data Formats: Matlab
- Tags: Dimensionality Reduction, Pca, Feature Extraction, Principal Component Analysis, Multilinear Subspace Learning, Tensor
- Archive: download here
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