Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Neural Computation
Learning Overcomplete Representations
Neural Computation
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Kernel independent component analysis
The Journal of Machine Learning Research
Variational learning of clusters of undercomplete nonsymmetric independent components
The Journal of Machine Learning Research
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
A Further Result on the ICA One-Bit-Matching Conjecture
Neural Computation
On Convergence Conditions of an Extended Projection Neural Network
Neural Computation
One-Bit-Matching Conjecture for Independent Component Analysis
Neural Computation
Analysis of the Kurtosis-Sum Objective Function for ICA
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Generalized discriminant analysis: a matrix exponential approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Selective noise cancellation using independent component analysis
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
One-Bit-Matching ICA theorem, convex-concave programming, and combinatorial optimization
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Two adaptive matching learning algorithms for independent component analysis
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Analysis of feasible solutions of the ICA problem under the one-bit-matching condition
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm. The source distributions are modeled as D one-dimensional mixtures of gaussians. The observed data are modeled as linear mixtures of the sources with additive, isotropic noise. This generative model is fit to the data using constrained EM. The simpler "soft-switching" approach is introduced, which uses only one parameter to decide on the sub- or supergaussian nature of the sources. We explain how our approach relates to independent factor analysis.