Analysis of sparse representation and blind source separation

Neural Computation

Volumn 16, Issue 6, 2004, Pages 1193-1234

  Li, Yuanqing ^a,b   Cichocki, Andrzej ^a,c   Amari, Shun Ichi ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^c WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; FACE; HUMAN; IMAGE PROCESSING; PHYSIOLOGY;

ALGORITHMS; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; FACE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 2442473143     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976604773717586     Document Type: Article

References (16)

1. Bofill, P., & Zibulevsky, M. (2001). Underdetermined blind source separation using sparse representations. Signal Processing, 81(11), 2353-2362.
2. Chen, S., Donoho, D. L., & Saunders, M. A. (1998). Automic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33-61.
3. Cichocki, A., & Amari, S. (2002). Adaptive blind signal and image processing. Chicester: Wiley. (Revised and corrected edition.)
4. Delgado, K. K., Murray, J. F., Rao, B. D., Engan, K., Lee, T. W., & Sejnowski, T. J. (2003). Dictionary learning algorithms for sparse representation. Neural Computation, 15, 349-396.
5. 1 minimization. Proc. Nat. Aca. Sci., 100, 2197-2202.
6. Girolami, M. (2001). A variational method for learning sparse and overcomplete representations. Neural Computation, 13(11), 2517-1532.
7. Jang, G. J., & Lee, T. W. (2003). A probabilistic approach to single channel source separation. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15, Cambridge, MA: MIT Press.
8. Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by nonnegative matrix factorization. Nature, 401(21), 788-791.
9. Lee, T. W., Lewicki, M. S., Girolami, M. & Sejnowski, T. J. (1999). Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letter, 6(4), 87-90.
10. Lewicki, M. S., & Sejnowski, T. J. (2000). Learning overcomplete representations. Neural Computation, 12(2), 337-365.
11. Makeig, S., Westerfield, M., Jung, T. P., Enghoff, S., Townsend J., Courchesne E., & Sejnowski T. J. (2002). Dynamic brain sources of visual evoked responses. Science, 295, 690-694.
12. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by VI? Vision Research, 37, 3311-3325.
13. Olshausen, B. A., Sallee, P., & Lewicki, M. S. (2001). Learning sparse image codes using a wavelet pyramid architecture. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in neural information processing systems, 12. Cambridge, MA: MIT Press.
14. Zibulevsky, M., Kisilev, P., Zeevi, Y.Y., & Pearlmutter, B. A. (2001). Blind source separation via multinode sparse representation. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, 13. Cambridge, MA: MIT Press.
15. Zibulevsky, M., & Pearlmutter, B. A. (2001). Blind source separation by sparse decomposition in a signal dictionary. Neural Computation, 13, 863-882.
16. Zibulevsky, M., Pearlmutter, B. A., Boll, P., & Kisilev, P. (2000). Blind source separation by sparse decomposition in a signal dictionary. In S. J. Roberts, and R. M. Everson, (Eds.), Independent component analysis: Principles and practice. Cambridge: Cambridge University Press.