Probability and statistics
A neural net for blind separation of nonstationary signals
Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation
Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
Fast sparse representation based on smoothed lo norm
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Sparse source separation with unknown source number
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
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One of the major problems in underdetermined Sparse Component Analysis (SCA) in the field of (semi) Blind Source Separation (BSS) is the appropriate estimation of the mixing matrix, A, in the linear model X=AS, especially where more than one source is active at each instant of time. Most existing algorithms require the restriction that at each instant (i.e. in each column of the source matrix S), there is at most one single dominant component. Moreover, these algorithms require that the number of sources must be determined in advance. In this paper, we proposed a new algorithm for estimating the matrix A, which does not require the restriction of single dominant source at each instant. Moreover, it is not necessary that the exact number of sources be known a priori.