2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
Equivalence of Non-Iterative Algorithms for Simultaneous Low Rank Approximations of Matrices
New York, NY
June 17-June 22
ISBN: 0-7695-2597-0
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.
Citation:
Kohei Inoue, Kiichi Urahama, "Equivalence of Non-Iterative Algorithms for Simultaneous Low Rank Approximations of Matrices," cvpr, vol. 1, pp.154-159, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06), 2006