Blind sparse source separation using cluster particle swarm optimization technique
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A robust blind sparse source separation algorithm using genetic algorithm to identify mixing matrix
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
A Graph Clustering Algorithm Based on Minimum and Normalized Cut
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
A robust blind sparse source separation algorithm using genetic algorithm to identify mixing matrix
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Locally Defined Principal Curves and Surfaces
The Journal of Machine Learning Research
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
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This letter proposes a clustering-based approach for solving the underdetermined (i.e., fewer mixtures than sources) postnonlinear blind source separation (PNL BSS) problem when the sources are sparse. Although various algorithms exist for the underdetermined BSS problem for sparse sources, as well as for the PNL BSS problem with as many mixtures as sources, the nonlinear problem in an underdetermined scenario has not been satisfactorily solved yet. The method proposed in this letter aims at inverting the different nonlinearities, thus reducing the problem to linear underdetermined BSS. To this end, first a spectral clustering technique is applied that clusters the mixture samples into different sets corresponding to the different sources. Then, the inverse nonlinearities are estimated using a set of multilayer perceptrons (MLPs) that are trained by minimizing a specifically designed cost function. Finally, transforming each mixture by its corresponding inverse nonlinearity results in a linear underdetermined BSS problem, which can be solved using any of the existing methods