Skip to main content
Conference Proceedings

A hierarchical learning rule for Independent Component Analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (1996) 1112 LNCS 525-530
DOI: 10.1007/3-540-61510-5_90
0Citations
Citations of this article
1Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In this paper, a two-layer neural network is presented that organizes itself to perform Independent Component Analysis (ICA). A hierarchical, nonlinear learning rule is proposed which allows to extract the unknown independent source signals out of a linear mixture. The convergence behaviour of the network is analyzed mathematically.

Cite

CITATION STYLE

APA

Freisleben, B., & Hagen, C. (1996). A hierarchical learning rule for Independent Component Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 525–530). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_90

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free