An Alternative Perspective on Adaptive Independent Component Analysis Algorithms

Authors:
Mark Girolami
Affiliations:
Department of Computing and Information Systems, University of Paisley, Paisley, PA1 2BE Scotland
Venue:
Neural Computation
Year:
1998

Citing 12
Cited 6

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Representation and separation of signals using nonlinear PCA type learning

Neural Networks
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Natural gradient works efficiently in learning

Neural Computation
The Latent Variable Data Model for Exploratory DataAnalysis and Visualisation: A Generalisation of theNonlinear Infomax Algorithm

Neural Processing Letters
Extended ICA removes artifacts from electroencephalographic recordings

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources

Neural Computation
Blind Source Separation Using Least-Squares Type Adaptive Algorithms

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Equivariant adaptive source separation

IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach

IEEE Transactions on Signal Processing

Flexible Independent Component Analysis

Journal of VLSI Signal Processing Systems
Complexity Pursuit: Separating Interesting Components from Time Series

Neural Computation
Classification of gene-expression data: The manifold-based metric learning way

Pattern Recognition
EEG-based subject- and session-independent drowsiness detection: an unsupervised approach

EURASIP Journal on Advances in Signal Processing
Computational intelligent brain computer interaction and its applications on driving cognition

IEEE Computational Intelligence Magazine
Letters: A fast fixed-point algorithm for complexity pursuit

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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 closedform 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.