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

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
出版ステータスPublished - 2012 2 27
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 122012 3 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)
7191 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
CountryIsrael
CityTel Aviv
Period12/3/1212/3/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

フィンガープリント 「Nonnegative matrix factorization via generalized product rule and its application for classification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル