Subpattern-based principle component analysis

被引：123

作者：

Chen, SC ^{[1
]}

Zhu, YL ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Engn, Nanjing 210016, Peoples R China

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 05期

关键词：

principle component analysis (PCA); subpattern PCA (SpPCA); feature extraction; pattern recognition;

D O I：

10.1016/j.patcog.2003.09.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a subpattern-based principle component analysis (SpPCA). The traditional PCA operates directly on a whole pattern represented as a vector and acquires a set of projection vectors to extract global features from given training patterns. SpPCA operates instead directly on a set of partitioned subpatterns of the original pattern and acquires a set of projection sub-vectors for each partition to extract corresponding local sub-features and then synthesizes them into global features for subsequent classification. The experimental results show that the proposed SpPCA has (much) better classification performances on all the real-life benchmark datasets than PICA. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1081 / 1083

页数：3