Results 1 
4 of
4
Independent Component Analysis
 Neural Computing Surveys
, 2001
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
Abstract

Cited by 1507 (93 self)
 Add to MetaCart
A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Wellknown linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA. 1
Fast and robust fixedpoint algorithms for independent component analysis
 IEEE TRANS. NEURAL NETW
, 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
Abstract

Cited by 518 (34 self)
 Add to MetaCart
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informationtheoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixedpoint algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.
Independent Component Analysis by General Nonlinear Hebbianlike Learning Rules
 Signal Processing
, 1998
"... A number of neural learning rules have been recently proposed... In this paper, we show that in fact, ICA can be performed by very simple Hebbian or antiHebbian learning rules, which may have only weak relations to such informationtheoretical quantities. Rather suprisingly, practically any nonlin ..."
Abstract

Cited by 57 (11 self)
 Add to MetaCart
A number of neural learning rules have been recently proposed... In this paper, we show that in fact, ICA can be performed by very simple Hebbian or antiHebbian learning rules, which may have only weak relations to such informationtheoretical quantities. Rather suprisingly, practically any nonlinear function can be used in the learning rule, provided only that the sign of the Hebbian/antiHebbian term is chosen correctly. In addition to the Hebbianlike mechanism, the weight vector is here constrained to have unit norm, and the data is preprocessed by prewhitening, or sphering. These results imply that one can choose the nonlinearity so as to optimize desired statistical or numerical criteria.
Simple Neuron Models for Independent Component Analysis
 Int. Journal of Neural Systems
, 1997
"... Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbianlike learning rules are introduced for estimating one of the independent components from sphered data. Some of th ..."
Abstract

Cited by 22 (3 self)
 Add to MetaCart
Recently, several neural algorithms have been introduced for Independent Component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbianlike learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a twounit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from nonsphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distributions of the independent components.