Dimensionality reduction by simultaneous low-rank approximation of matrix data

Kohei Inoue, Kiichi Urahama

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    The authors propose a method of obtaining a direct solution of a simultaneous low-rank approximation of multiple matrices by using a tensor-matrix product relation to derive a method of obtaining an approximate solution of an optimization problem for performing a simultaneous low-rank approximation of multiple matrices from a higher-order singular value decomposition of a tensor. Since the proposed method is a direct solution method, it is faster than a generalized low-rank approximation algorithm, which is an iterative solution method. They apply the proposed method to face image recognition to show experimentally that its recognition rate is higher and its processing is faster than the eigenface method. They also applied it to an image similarity search to show experimentally that its precision is higher than a dimensionality reduction method for simply reducing the image size.

    Original languageEnglish
    Pages (from-to)42-49
    Number of pages8
    JournalElectronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
    Volume90
    Issue number9
    DOIs
    Publication statusPublished - Sept 1 2007

    All Science Journal Classification (ASJC) codes

    • General Physics and Astronomy
    • Computer Networks and Communications
    • Electrical and Electronic Engineering

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