Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
A Further Result on the ICA One-Bit-Matching Conjecture
Neural Computation
One-Bit-Matching Conjecture for Independent Component Analysis
Neural Computation
A Constrained EM Algorithm for Independent Component Analysis
Neural Computation
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
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
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Self-adaptive blind source separation based on activation functions adaptation
IEEE Transactions on Neural Networks
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The majority of existing Independent Component Analysis (ICA) algorithms are based on maximizing or minimizing a certain objective function with the help of gradient learning methods. However, it is rather difficult to prove whether there is no spurious solution in ICA under any objective function as well as the gradient learning algorithm to optimize it. In this paper, we present an analysis on the kurtosis-sum objective function, i.e., the sum of the absolute kurtosis values of all the estimated components, with a kurtosis switching algorithm to maximize it. In two-source case, it is proved that any local maximum of this kurtosis-sum objective function corresponds to a feasible solution of the ICA problem in the asymptotic sense. The simulation results further show that the kurtosis switching algorithm always leads to a feasible solution of the ICA problem for various types of sources.