A fast fixed-point algorithm for independent component analysis
Neural Computation
Minimum support ICA using order statistics. part I: quasi-range based support estimation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Filtering-Free blind separation of correlated images
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Blind separation of instantaneous mixture of sources based on orderstatistics
IEEE Transactions on Signal Processing
Algorithms for nonnegative independent component analysis
IEEE Transactions on Neural Networks
Minimum support ICA using order statistics. part I: quasi-range based support estimation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Linear instantaneous independent component analysis (ICA) is a well-known problem, for which efficient algorithms like FastICA and JADE have been developed. Nevertheless, the development of new contrasts and optimization procedures is still needed, e.g. to improve the separation performances in specific cases. For example, algorithms may exploit prior information, such as the sparseness or the non-negativity of the sources. In this paper, we show that support-width minimization-based ICA algorithms may outperform other well-known ICA methods when extracting bounded sources. The output supports are estimated using symmetric differences of order statistics.