Title :
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
fDate :
30 Aug-3 Sep 1992
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;
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
DOI :
10.1109/ICPR.1992.201724
