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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages263-271
Number of pages9
DOIs
Publication statusPublished - 2012 Feb 27
Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
Duration: 2012 Mar 122012 Mar 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7191 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

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

Keywords

  • Nonnegative matrix factorization
  • classification
  • generalized product rule
  • nonlinear function
  • subspace method

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Nonnegative matrix factorization via generalized product rule and its application for classification'. Together they form a unique fingerprint.

  • 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