Analysis of sparse representation and blind source separation
Neural Computation
K-EVD clustering and its applications to sparse component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Performance analysis of minimum ℓ1-norm solutions for underdetermined source separation
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper the K-hyperplanes clustering problem is discussed and we present a K-hyperplanes clustering algorithm, which can be applied to sparse component analysis (SCA) for linear model X=AS+V, where X is a m by T matrix of observation, A is an unknown m by n basis matrix and S is an unknown n by T matrix of sparse sources. The proposed algorithm is suitable for a relaxed case when more than one source signal achieves significant value at any time instant. More precisely, in this paper we propose a new algorithm which is suitable for the case when the (m-1) source signals are simultaneously nonzero for sufficient number of samples, where m is the number of observation. In contrast to the conventional SCA algorithm which is based on the assumption that for each time, there is only one dominant component and others components are not significant. We assume that the sources can be only moderately sparse. However, the complexity of the algorithm is higher than that of the conventional SCA algorithms. We confirmed the validity and good performance of the proposed algorithm by computer simulation.