Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Neural Computation
A Constrained EM Algorithm for Independent Component Analysis
Neural Computation
A Further Result on the ICA One-Bit-Matching Conjecture
Neural Computation
Independent Component Analysis for Time-dependent Processes Using AR Source Model
Neural Processing Letters
Letters: Gaussian moments for noisy unifying model
Neurocomputing
Analysis of the Kurtosis-Sum Objective Function for ICA
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Fast nonlinear autocorrelation algorithm for source separation
Pattern Recognition
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
A step by step optimization approach to independent component analysis
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
Mining the independent source of ERP components with ICA decomposition
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A one-bit-matching learning algorithm for independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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
Local stability analysis of maximum nongaussianity estimation in independent component analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Global convergence of FastICA: theoretical analysis and practical considerations
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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The one-bit-matching conjecture for independent component analysis (ICA) could be understood from different perspectives but is basically stated as "all the sources can be separated as long as there is a one-to-one same-sign-correspondence between the kurtosis signs of all source probability density functions (pdf's) and the kurtosis signs of all model pdf's" (Xu, Cheung, & Amari, 1998a). This conjecture has been widely believed in the ICA community and implicitly supported by many ICA studies, such as the Extended Infomax (Lee, Girolami, & Sejnowski, 1999) and the soft switching algorithm (Welling & Weber, 2001). However, there is no mathematical proof to confirm the conjecture theoretically. In this article, only skewness and kurtosis are considered, and such a mathematical proof is given under the assumption that the skewness of the model densities vanishes. Moreover, empirical experiments are demonstrated on the robustness of the conjecture as the vanishing skewness assumption breaks. As a by-product, we also show that the kurtosis maximization criterion (Moreau & Macchi, 1996) is actually a special case of the minimum mutual information criterion for ICA.