Equivalence of non-iterative algorithms for simultaneous low rank approximations of matrices

Kohei Inoue, Kiichi Urahama

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

20 Citations (Scopus)

Abstract

Recently four non-iterative algorithms for simultaneous low rank approximations of matrices (SLRAM) have been presented by several researchers. In this paper, we show that those algorithms are equivalent to each other because they are reduced to the eigenvalue problems of row-row and column-column covariance matrices of given matrices. Also, we show a relationship between the non-iterative algorithms and another algorithm which is claimed to be an analytical algorithm for the SLRAM, Experimental results show that the analytical algorithm does not necessarily give the optimal solution of the SLRAM.

Original languageEnglish
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages154-159
Number of pages6
DOIs
Publication statusPublished - Dec 22 2006
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
ISSN (Print)1063-6919

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
CountryUnited States
CityNew York, NY
Period6/17/066/22/06

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Equivalence of non-iterative algorithms for simultaneous low rank approximations of matrices'. Together they form a unique fingerprint.

  • Cite this

    Inoue, K., & Urahama, K. (2006). Equivalence of non-iterative algorithms for simultaneous low rank approximations of matrices. In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 (pp. 154-159). [1640754] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 1). https://doi.org/10.1109/CVPR.2006.112