An analytical algorithm for generalized low-rank approximations of matrices

被引：15
|
作者：
Liang, ZZ [1 ]
Shi, PF [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China
来源：
PATTERN RECOGNITION | 2005年 / 38卷 / 11期
关键词：
low-rank approximations of matrices; 2DPCA; the optimization function;
D O I：
10.1016/j.patcog.2005.04.012
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The algorithm for generalized low-rank approximations of matrices (GLRAM) has been developed recently. In this paper, the optimality property of GLRAM is revealed. Accordingly, an analytical method for GLRAM is proposed. The proposed method is non-iterative. Moreover, the relationship between 2DPCA and GLRAM is shown. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2213 / 2216
页数：4
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