Symmetric generalized low rank approximations of matrices

Kohei Inoue, Kenji Hara, Kiichi Urahama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    Recently, the generalized low rank approximations of matrices (GLRAM) have been proposed for dimensionality reduction of matrices such as images. However, in GLRAM, it is necessary for users to specify the numbers of rows and columns in low rank matrices. In this paper, we propose a method for determining them semiautomatically by symmetrizing GLRAM. Experimental results show that the proposed method can determine the optimal ranks of matrices while achieving competitive approximation performance.

    Original languageEnglish
    Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
    Pages949-952
    Number of pages4
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
    Duration: Mar 25 2012Mar 30 2012

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149

    Other

    Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
    Country/TerritoryJapan
    CityKyoto
    Period3/25/123/30/12

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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