Estimating the mixing matrix in Sparse Component Analysis (SCA) based on partial k-dimensional subspace clustering

Author:

Movahedi Naini Farid,Hosein Mohimani G.,Babaie-Zadeh Massoud,Jutten Christian

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference24 articles.

1. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation;Aharon;IEEE Trans. Signal Process.,2006

2. A.A. Amini, M. Babaie-Zadeh, C. Jutten, A fast method for sparse component analysis based on iterative detection-projection, in: Proceedings of 26th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt), 2006.

3. M. Babaie-Zadeh, C. Jutten, Semi-blind approaches for source separation and independent component analysis, in: Proceedings of ESANN’06, 2006, pp. 301–312.

4. A. Belouchrani, J.-F. Cardoso, Maximum likelihood source separation by the expectation-maximization technique, in: NOLTA 95, Las Vegas, USA, 1995, pp. 49–53.

5. P. Bofill, M. Zibulevsky, Underdetermined blind source separation using sparse representations, Signal Processing 81 (2001) 2353–2362 〈citeseer.ist.psu.edu/bofill01underdetermined.html〉.

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