DocumentCode
3147250
Title
Symmetric generalized low rank approximations of matrices
Author
Inoue, Kohei ; Hara, Kenji ; Urahama, Kiichi
Author_Institution
Dept. of Commun. Design Sci., Kyushu Univ., Fukuoka, Japan
fYear
2012
fDate
25-30 March 2012
Firstpage
949
Lastpage
952
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.
Keywords
approximation theory; image processing; matrix algebra; GLRAM; competitive approximation performance; dimensionality reduction; low rank matrices; symmetric generalized low rank approximations; Abstracts; Approximation methods; Matrix decomposition; Dimensionality reduction; GLRAM; matrices; symmetric GLRAM;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location
Kyoto
ISSN
1520-6149
Print_ISBN
978-1-4673-0045-2
Electronic_ISBN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2012.6288042
Filename
6288042
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