A new method for unsupervised linear feature extraction, using fourth order moments

Author

Lenz, Reiner ; Österberg, Mats

Author_Institution

Image Process. Lab., Linkoping Univ., Sweden

fYear

1992

fDate

30 Aug-3 Sep 1992

Firstpage

71

Lastpage

74

Abstract

The authors discuss two classes of unsupervised feature extraction methods. They show that a system based on second order moments can learn the Karhunen-Loeve expansion in parallel. Then they show that systems based on second order moments only have important drawbacks. The second class of systems described avoids this problem by using fourth order moments. Since these systems are much harder to analyze the authors demonstrate some of their advantages with the help of some experiments

Keywords

feature extraction; image recognition; learning systems; neural nets; Karhunen-Loeve expansion; fourth order moments; learning systems; neural nets; pattern recognition; second order moments; unsupervised linear feature extraction; Covariance matrix; Eigenvalues and eigenfunctions; Feature extraction; Fourier series; Hilbert space; Image processing; Laboratories; Nonlinear filters; Signal design; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on

Conference_Location

The Hague

Print_ISBN

0-8186-2915-0

Type

conf

DOI

10.1109/ICPR.1992.201724

Filename

201724