Nonnegative matrix factorization via generalized product rule and its application for classification

Yu Fujimoto; Noboru Murata

doi:10.1007/978-3-642-28551-6_33

Nonnegative matrix factorization via generalized product rule and its application for classification

Yu Fujimoto^*, Noboru Murata

^*Corresponding author for this work

School of Advanced Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Nonnegative Matrix Factorization (NMF) is broadly used as a mathematical tool for processing tasks of tabulated data. In this paper, an extension of NMF based on a generalized product rule, defined with a nonlinear one-parameter function and its inverse, is proposed. From a viewpoint of subspace methods, the extended NMF constructs flexible subspaces which plays an important role in classification tasks. Experimental results on benchmark datasets show that the proposed extension improves classification accuracies.

Original language	English
Title of host publication	Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages	263-271
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-28551-6_33
Publication status	Published - 2012 Feb 27
Event	10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel Duration: 2012 Mar 12 → 2012 Mar 15

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7191 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Country/Territory	Israel
City	Tel Aviv
Period	12/3/12 → 12/3/15

Keywords

Nonnegative matrix factorization
classification
generalized product rule
nonlinear function
subspace method

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-28551-6_33

Cite this

Fujimoto, Y., & Murata, N. (2012). Nonnegative matrix factorization via generalized product rule and its application for classification. In Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings (pp. 263-271). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7191 LNCS). https://doi.org/10.1007/978-3-642-28551-6_33

Nonnegative matrix factorization via generalized product rule and its application for classification. / Fujimoto, Yu; Murata, Noboru.
Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings. 2012. p. 263-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7191 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fujimoto, Y & Murata, N 2012, Nonnegative matrix factorization via generalized product rule and its application for classification. in Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7191 LNCS, pp. 263-271, 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012, Tel Aviv, Israel, 12/3/12. https://doi.org/10.1007/978-3-642-28551-6_33

Fujimoto Y, Murata N. Nonnegative matrix factorization via generalized product rule and its application for classification. In Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings. 2012. p. 263-271. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-28551-6_33

Fujimoto, Yu ; Murata, Noboru. / Nonnegative matrix factorization via generalized product rule and its application for classification. Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings. 2012. pp. 263-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Nonnegative matrix factorization via generalized product rule and its application for classification

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