Nonlinear PCA Type Approaches for Source Separation and Independent Component Analysis (1995)
| Venue: | In Proc. ICNN |
| Citations: | 22 - 3 self |
BibTeX
@INPROCEEDINGS{Karhunen95nonlinearpca,
author = {Juha Karhunen and Liuyue Wang and Ricardo Vigario},
title = {Nonlinear PCA Type Approaches for Source Separation and Independent Component Analysis},
booktitle = {In Proc. ICNN},
year = {1995},
pages = {995--1000}
}
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Abstract
In this paper, we study the application of some nonlinear neural PCA type approaches to the separation of independent source signals from their linear mixture. This problem is important in signal processing and communications, and it cannot be solved using standard PCA. Using prewhitening and appropriate choice of nonlinearities, several algorithms proposed by us yield good separation results for sub-Gaussian (or super-Gaussian) source signals. We discuss the related problem of estimating the basis vectors in Independent Component Analysis briefly, too. 1. Introduction Principal Component Analysis (PCA) is a standard statistical technique which is defined in terms of the largest eigenvalues and the respective eigenvectors of the covariance matrix of the input data. PCA is used in many applications because of its optimality properties in data compression and information representation [10]. It is now well-known that PCA can be realized neurally in various ways [3, 16]. Currently...
