Description:

This archive contains a Matlab implementation of the Uncorrelated Multilinear Principal Component Analysis (UMPCA) algorithm, as described in the paper:

Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning", IEEE Transactions on Neural Networks,

Vol. 20, No. 11, Page: 1820-1836, Nov. 2009.

%[Usages]%

Please refer to the comments in the codes for example usage on 2D data "FERETC70A15S8_80x80" in the directory "FERETC70A15S8", which is used in the paper above. Various partitions used in the paper are included in the directory "FERETC70A15S8" for L=1 to 7.

Directory "USFGait17_32x22x10" contains the gait data used in the paper above.

%[Toolbox needed]%:

This code needs the tensor toolbox available at http://csmr.ca.sandia.gov/~tgkolda/TensorToolbox/

This package includes tensor toolbox version 2.1 for convenience.

%[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, "Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning", IEEE Transactions on Neural Networks,

Vol. 20, No. 11, pp. 1820-1836, Nov. 2009.

%[Additional Resources]%

The BibTeX file "MPCApublications.bib" contains the BibTex for UMPCA and related works. The included survey paper "SurveyMSL_PR2011.pdf" discusses the relations between UMPCA and related works.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

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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