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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages263-271
    Number of pages9
    Volume7191 LNCS
    DOIs
    Publication statusPublished - 2012
    Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv
    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)03029743
    ISSN (Electronic)16113349

    Other

    Other10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
    CityTel Aviv
    Period12/3/1212/3/15

    Fingerprint

    Product rule
    Non-negative Matrix Factorization
    Factorization
    Subspace Methods
    Subspace
    Benchmark
    Experimental Results
    Processing

    Keywords

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

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Fujimoto, Y., & Murata, N. (2012). Nonnegative matrix factorization via generalized product rule and its application for classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7191 LNCS, 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.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7191 LNCS 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 proceedingConference contribution

    Fujimoto, Y & Murata, N 2012, Nonnegative matrix factorization via generalized product rule and its application for classification. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7191 LNCS, 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, 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7191 LNCS. 2012. p. 263-271. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-28551-6_33
    Fujimoto, Yu ; Murata, Noboru. / Nonnegative matrix factorization via generalized product rule and its application for classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7191 LNCS 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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