Discriminant Non-Negative Matrix Factorization

Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.


DNMF version 1.2 (2015-06-09)

  • debug NOTEs (no visible binding for global variable).

DNMF version 1.2 (2015-06-09)

  • revise typo in manual

DNMF version 1.1 (2015-05-13)

  • add nDNMF and NMFpval functions.

  • init H reasonably.

  • options to deal with multi-class for rnk

DNMF version 1.0 (2015-02-16)

  • Submit DNMF to CRAN.

Reference manual

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1.3 by Zhilong Jia, 4 years ago


Report a bug at https://github.com/zhilongjia/DNMF/issues

Browse source code at https://github.com/cran/DNMF

Authors: Zhilong Jia [aut, cre] , Xiang Zhang [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Matrix, gplots, parallel, doParallel

Depends on foreach

See at CRAN