High-order contrasts for independent component analysis
Neural Computation
Neural Computation
Flexible Independent Component Analysis
Journal of VLSI Signal Processing Systems
Adaptive Score Functions for Maximum Likelihood ICA
Journal of VLSI Signal Processing Systems
Contravariant adaptation on structured matrix spaces
Signal Processing
Blind separation methods based on Pearson system and its extensions
Signal Processing
Dictionary learning algorithms for sparse representation
Neural Computation
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Blind separation of jointly stationary correlated sources
Signal Processing - Special issue on independent components analysis and beyond
Minimax mutual information approach for independent component analysis
Neural Computation
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Signal Processing - Special issue: Information theoretic signal processing
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
A general procedure for learning mixtures of independent component analyzers
Pattern Recognition
A source adaptive independent component analysis algorithm through solving the estimating equation
Expert Systems with Applications: An International Journal
Blind signal separation and identification of mixtures of images
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Adaptive independent component analysis by modified Kernel density estimation
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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In this paper, we introduce a procedure for separating a multivariate distribution into nearly independent components based on minimizing a criterion defined in terms of the Kullback-Leibner distance. By replacing the unknown density with a kernel estimate, we derive useful forms of this criterion when only a sample from that distribution is available. We also compute the gradient and Hessian of our criteria for use in an iterative minimization. Setting this gradient to zero yields a set of separating functions similar to the ones considered in the source separation problem, except that here, these functions are adapted to the observed data. Finally, some simulations are given, illustrating the good performance of the method