Description:

This archive contains a Matlab implementation of the Uncorrelated Multilinear Discriminant Analysis (UMLDA) algorithm (as well as its regularized and aggregated versions), as described in the paper:

Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Uncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition", IEEE Transactions on Neural Networks, Vol. 20, No. 1, Page: 103-123, Jan. 2009.

%[Data]%

All data used in the paper are included in this package, except the PIE faces due to the 10MB size limit:

Directory "FERETC80A45S6" contains the FERET face data for C=80 and their partitions. Directory "FERETC160A45S6" contains the FERET face data for C=160 and their partitions. Directory "FERETC240A45S6" contains the FERET face data for C=240 and their partitions. Directory "FERETC320A45S6" contains the FERET face data for C=320 and their partitions. Directory "USFGait17_32x22x10" contains the gait data used in the paper.

The PIE face data used in the paper can be downloaded from http://www.dsp.toronto.edu/~haiping/CodeData/piep3i3.zip

%[Usages]%

Please refer to "demoR-UMLDA-Aggr.m" for example usage on 2D data "FERETC80A45S6_32x32" in the directory "FERETC80A45S6", which is used in the paper above. The partition used in the paper is included in the directory "FERETC80A45S64Train" for L=4.

%[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 Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition", IEEE Transactions on Neural Networks, Vol. 20, No. 1, Page: 103-123, Jan. 2009.

%[Additional Resources]%

The BibTeX file "UMLDApublications" contains the BibTex for UMLDA and related works. The included survey paper "SurveyMSL_PR2011.pdf" discusses the relations between UMLDA 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: Lda, Dimensionality Reduction, Feature Extraction, Linear Discriminant Analysis, Multilinear Subspace Learning, Tensor, Subspace Learning

Archive: download here

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.