Multiresolution using principal component analysis

Author

Brennan, Vic ; Principe, Jose

Author_Institution

Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA

Volume

6

fYear

2000

fDate

2000

Firstpage

3474

Abstract

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 single layer linear network using the generalized Hebbian algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully applied to face classification. Good results with biological signals have also been reported

Keywords

Hebbian learning; feature extraction; image classification; image coding; image representation; image resolution; principal component analysis; PCA; biological signals; compressed images; face classification; feature extraction; generalized Hebbian algorithm; input image decomposition; multiresolution; multiresolution PCA; principal component analysis; signal dependent representations; single layer linear network; Eigenvalues and eigenfunctions; Energy resolution; Equations; Image databases; Matrix decomposition; Neural engineering; Principal component analysis; Scattering; Signal resolution; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.860149

Filename

860149