An Alternative Perspective on Adaptive Independent Component Analysis Algorithms-Reference-Cited by-同舟云学术

An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

Published:1998-11-01 Issue:8 Volume:10 Page:2103-2114
ISSN:0899-7667
Container-title:Neural Computation
language:en
Short-container-title:Neural Computation

Author:

Girolami Mark¹

Affiliation:

1. Department of Computing and Information Systems, University of Paisley, Paisley, PA1 2BE Scotland

Abstract

This article develops an extended independent component analysis algorithm for mixtures of arbitrary subgaussian and supergaussian sources. The gaussian mixture model of Pearson is employed in deriving a closed-form generic score function for strictly subgaussian sources. This is combined with the score function for a unimodal supergaussian density to provide a computationally simple yet powerful algorithm for performing independent component analysis on arbitrary mixtures of nongaussian sources.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/089976698300016981

Reference17 articles.

1. Natural Gradient Works Efficiently in Learning

2. Blind source separation-semiparametric statistical approach

3. An Information-Maximization Approach to Blind Separation and Blind Deconvolution

4. The “independent components” of natural scenes are edge filters

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