Multiresolution using principal component analysis

被引：0
|
作者：
Brennan, V [1 ]
Principe, J [1 ]
机构：
[1] Univ Florida, Dept Elect Engn, Computat NeuroEngn Lab, Gainesville, FL 32611 USA
来源：
2000 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS, VOLS I-VI | 2000年
关键词：
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
This paper proposes Principal Component Analysis (PCA) to find adaptive bases for multiresolution. An input image is decomposed into components (compressed images) which are uncorrelated and have maximum l(2) energy. With only minor modification, a simple layer linear network using the Generalized Hebbian Algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully applied to face classification [3]. Good results with biological signals have also been reported [1].
引用
收藏
页码：3474 / 3477
页数：4
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