Linear and nonlinear ICA based on mutual information
- 7 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
In the context of independent components analysis (ICA), the mutual information (MI) of the extracted components is one of the most desirable measures of independence, due to its special properties. This paper presents a method for performing linear and nonlinear ICA based on MI, with few approximations. The use of MI as an objective function for ICA requires the estimation of the statistical distributions of the separated components. In this work, both the extraction of independent components and the estimation of their distributions are performed simultaneously, by a single network with a specialized structure, trained with a single objective function.Keywords
This publication has 5 references indexed in Scilit:
- Nonlinear independent component analysis: Existence and uniqueness resultsNeural Networks, 1999
- Nonlinear Independent Component Analysis by self-organizing mapsPublished by Springer Nature ,1996
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995
- Nonlinear higher-order statistical decorrelation by volume-conserving neural architecturesNeural Networks, 1995
- Analyse générale des liaisons stochastiques: etude particulière de l'analyse factorielle linéaireRevue de l'Institut International de Statistique / Review of the International Statistical Institute, 1953