Multilinear principal component analysis: Difference between revisions

Revision as of 22:34, 26 November 2016

Revision as of 13:52, 10 December 2016

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*Non-negative MPCA (NMPCA) <ref>Y. Panagakis, C. Kotropoulos, G. R. Arce, "Non-negative multilinear principal component analysis of auditory temporal modulations for music genre classification", IEEE Trans. on Audio, Speech, and Language Processing, vol. 18, no. 3, pp. 576–588, 2010.</ref>

*Robust MPCA (RMPCA) <ref>K. Inoue, K. Hara, K. Urahama, "Robust multilinear principal component analysis", Proc. IEEE Conference on Computer Vision, 2009, pp. 591–597.</ref>

*Multi-Tensor Factorization, that also finds the number of components automatically (MTF) <ref>{{Cite journal|last=Khan|first=Suleiman A.|last2=Leppäaho|first2=Eemeli|last3=Kaski|first3=Samuel|date=2016-06-10|title=Bayesian multi-tensor factorization|url=http://link.springer.com/article/10.1007/s10994-016-5563-y|journal=Machine Learning|language=en|volume=105|issue=2|pages=233–253|doi=10.1007/s10994-016-5563-y|issn=0885-6125}}</ref>

== Resources ==

* '''Matlab code''': [http://www.mathworks.com/matlabcentral/fileexchange/26168 MPCA].

* '''Matlab code''': [http://www.mathworks.com/matlabcentral/fileexchange/35432 UMPCA (including data)].

* '''R code:''' [http://research.cs.aalto.fi/pml/software/mtf/ MTF]

==References==