mloss.org Multilinear Principal Component Analysishttp://mloss.orgUpdates and additions to Multilinear Principal Component AnalysisenSun, 08 Sep 2013 13:04:03 -0000Multilinear Principal Component Analysis 1.3http://mloss.org/software/view/400/<html><p>This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper
</p>
<p>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.
</p>
<p>Algorithm 1: "MPCA.m" implements the MPCA algorithm described in this paper.
</p>
<h2>Algorithm 2: "MPCALDA.m" implements the MPCA+LDA algorithm in this paper.</h2>
<p>%[Usages]%
Please refer to the comments in the codes, which include example usage on 2D data and 3D data below:
</p>
<p>FERETC80A45.mat: 320 faces (32x32) of 80 subjects (4 samples per class) from the FERET database
</p>
<h2>USF17Gal.mat: 731 gait samples (32x22x10) of 71 subjects from the gallery set of the USF gait challenge data sets version 1.7</h2>
<p>%[Verification of gait recognition results]%
To verify the gait recognition results presented in Table VII of the paper on a smaller version of the gait data in folder "USFGait17_32x22x10" so the numbers are not exactly the same
</p>
<ol>
<li>
Run GRTestMPCA.m to get the results for ETG
</li>
<li>
Run GRTestMPCALDA.m to get the results for ETGLDA
</li>
</ol>
<p>testData.m specifies the data directory and probes to be processed
</p>
<p>MADAll.m calculates the rank 1 and rank 5 identification rates using MAD measure (Table II) and symmetric matching.
</p>
<h2>GRResultsVerify.txt is the expected output in the command window.</h2>
<p>%[Toolbox]%
The code needs the tensor toolbox available at http://csmr.ca.sandia.gov/~tgkolda/TensorToolbox/
</p>
<h2>This package includes tensor toolbox version 2.1 for convenience.</h2>
<p>%[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:
</p>
<p>[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.
</p></html>Haiping LuSun, 08 Sep 2013 13:04:03 -0000http://mloss.org/software/rss/comments/400http://mloss.org/software/view/400/dimensionality reductionpcafeature extractionprincipal component analysismultilinear subspace learningtensor