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

Neurocomputing

Volumn 71, Issue 10-12, 2008, Pages 2330-2343

  Movahedi Naini, Farid ^a   Hosein Mohimani, G ^a   Babaie Zadeh, Massoud ^a   Jutten, Christian ^b  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^b GIPSA LAB   (France)

Author keywords

Sparse Component Analysis (SCA); Subspace fitting; Underdetermined Blind Source Separation (BSS)

Indexed keywords

ALGORITHMS; INDEPENDENT COMPONENT ANALYSIS; MATRIX ALGEBRA;

SPARSE COMPONENT ANALYSIS; SUBSPACE FITTING;

BLIND SOURCE SEPARATION;

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SIMULATION; STATISTICAL MODEL;

EID: 44649197165     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.07.035     Document Type: Article

References (24)

1. Aharon M., Elad M., and Bruckstein A. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54 11 (2006) 4311-4322
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〉.
6. Cichocki A., and Amari S.-i. Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications (2002), Wiley, New York
7. 1-norm solution is also the sparsest solution, Technical Report 〈 http://www.stat.stanford.edu/∼donoho/Reports/2004/〉, 2004.
8. 1 norm minimization, Proc. Natl. Acad. Sci. 100 (5) (2003) 2197-2202 〈citeseer.ist.psu.edu/pati93orthogonal.html〉.
9. P.G. Georgiev, F. J. Theis, A. Cichocki, Blind source separation and sparse component analysis for over-complete mixtures, in: Proceedings of ICASSP'04, Montreal, Canada, 2004, pp. 493-496.
10. Georgiev P.G., Theis F.J., and Cichocki A. Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Trans. Neural Networks 16 4 (2005) 992-996
11. R. Gribonval, S. Lesage, A survey of sparse component analysis for blind source separation: principles, perspectives, and new challenges, in: Proceedings of ESANN'06, 2006, pp. 323-330.
12. Hyvarinen A., Karhunen J., and Oja E. Independent Component Analysis (2001), Wiley, New York
13. Lee T., Lewicki M., Girolami M., and Sejnowski T. Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Process. Lett. 4 4 (1999) 87-90
14. Y. Li, A. Cichocki, S. Amari, Sparse component analysis for blind source separation with less sensors than sources, in: ICA2003, 2003, pp. 89-94.
15. Li Y., Cichocki A., and Amari S. Analysis of sparse representation and blind source separation. Neural Computation 16 6 (2004) 1193-1234
16. Matsuoka K., Ohya M., and Kawamoto M. A neural net for blind separation of nonstationary signals. Neural Networks 8 3 (1995) 411-419
17. Molgedey L., and Schuster H. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72 23 (1994) 3634-3637
18. N. Noorshams, M. Babaie-Zadeh, C. Jutten, Estimating the mixing matrix in Sparse Component Analysis (SCA) based on converting a multiple dominant to a single dominant problem, in: Proceedings of the Seventh International Conference on Independent Component Analysis and Signal Separation (ICA2007), Lecture Notes in Computer Science, vol. 4666, September 2007, London, pp. 397-405.
19. Papoulis A. Probability and Statistics (1990), Prentice-Hall, Englewood Cliffs, NJ
20. L. Tong, V. Soon, R. Liu, Y. Huang, AMUSE: a new blind identification algorithm, in: Proceedings of ISCAS, New Orleans, USA, 1990, pp. 1784-1786.
21. L. Vielva, Y. Pereiro, D. Erdogmus, J. Principe, Inversion techniques for underdetermined BSS in an arbitrary number of dimensions, in: Proceedings of the 6th International Conference on Independent Component Analysis and Signal Separation (ICA'03), Nara, Japan, 2003, pp. 131-136.
22. Y. Washizawa, A. Cichocki, On-Line k-plane clustering learning algorithm for sparse component analysis, in: Proceedings of ICASSP'06, Toulouse, France, 2006, pp. 681-684.
23. M. Zibulevsky, B.A. Pearlmutter, Blind source separation by sparse decomposition in a signal dictionary, Neural Computation 13 (4) (2001) 863-882 〈citeseer.ist.psu.edu/article/zibulevsky01blind.html〉.
24. M. Zibulevsky, B. Pearlmutter, P. Bofill, P. Kisilev, Blind source separation by sparse decomposition, in: Independent Component Analysis: Principles and Practice, Cambridge, 2001.