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

^*この研究の対応する著者

先進理工学部

研究成果: Conference contribution

抄録

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.

本文言語	English
ホスト出版物のタイトル	Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
ページ	263-271
ページ数	9
DOI	https://doi.org/10.1007/978-3-642-28551-6_33
出版ステータス	Published - 2012
イベント	10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel 継続期間: 2012 3月 12 → 2012 3月 15

出版物シリーズ

名前	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
巻	7191 LNCS
ISSN（印刷版）	0302-9743
ISSN（電子版）	1611-3349

Conference

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

ASJC Scopus subject areas

理論的コンピュータサイエンス
コンピュータサイエンス一般

文献へのアクセス

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

他のファイルとリンク

引用スタイル

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).

研究成果: 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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出版物シリーズ

Conference

ASJC Scopus subject areas

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