Uncorrelated Multilinear Discriminant Analysis

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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.

[Files]
RUMLDA.m: the Regularized UMLDA (R-UMLDA)
demoR-UMLDA-Aggr.m: sample code for R-UMLDA aggregation
estMaxSWEV.m: estimate \lambda_{max} in the paper, used for regularization

%[Data]%

All data used in the paper are included in this package:

Directory "PIEP3I3" contains the PIE face data and their partitions used in the paper.
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.

%[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.
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