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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2012.6288042
