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Computational Intelligence and Neuroscience
Volume 2008 (2008), Article ID 168769, 10 pages
Research Article

Pattern Expression Nonnegative Matrix Factorization: Algorithm and Applications to Blind Source Separation

1School of Computer Science and Engineering, Xidian University, Xi'an 710071, China
2Department of Mathematics and Computer Science, Valdosta State University, Valdosta, GA 31698, USA
3The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA 24061, USA

Received 1 November 2007; Accepted 18 April 2008

Academic Editor: Rafal Zdunek

Copyright © 2008 Junying Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, New York, NY, USA, 2001.
  2. P. O. Hoyer and A. Hyvärinen, “Independent component analysis applied to feature extraction from colour and stereo images,” Network: Computation in Neural Systems, vol. 11, no. 3, pp. 191–210, 2000. View at Publisher · View at Google Scholar
  3. J. Zhang, L. Wei, and Y. Wang, “Computational decomposition of molecular signatures based on blind source separation of non-negative dependent sources with NMF,” in Proceedings of the 13th IEEE Workshop on Neural Networks for Signal Processing (NNSP '03), pp. 409–418, Toulouse, France, September 2003.
  4. A. Cichocki, R. Zdunek, and S. Amari, “New algorithms for non-negative matrix factorization in applications to blind source separation,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06), vol. 5, pp. 621–624, Toulouse, France, May 2006. View at Publisher · View at Google Scholar
  5. A. Cichocki and R. Zdunek, “Multilayer nonnegative matrix factorization,” Electronics Letters, vol. 42, no. 6, pp. 947–948, 2006. View at Publisher · View at Google Scholar
  6. A. Pascual-Montano, J. M. Carazo, K. Kochi, D. Lehmann, and R. D. Pascual-Marqui, “Nonsmooth nonnegative matrix factorization (nsNMF),” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 403–415, 2006. View at Publisher · View at Google Scholar
  7. R. Zdunek and A. Cichocki, “Nonnegative matrix factorization with constrained second-order optimization,” Signal Processing, vol. 87, no. 8, pp. 1904–1916, 2007. View at Publisher · View at Google Scholar
  8. I. Kopriva, D. J. Garrood, and V. Borjanović, “Single frame blind image deconvolution by non-negative sparse matrix factorization,” Optics Communications, vol. 266, no. 2, pp. 456–464, 2006. View at Publisher · View at Google Scholar
  9. W. Liu and N. Zheng, “Non-negative matrix factorization based methods for object recognition,” Pattern Recognition Letters, vol. 25, no. 8, pp. 893–897, 2004. View at Publisher · View at Google Scholar
  10. D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, 1999. View at Publisher · View at Google Scholar
  12. P. Földiák, “Forming sparse representations by local anti-Hebbian learning,” Biological Cybernetics, vol. 64, no. 2, pp. 165–170, 1990. View at Publisher · View at Google Scholar
  13. J. Khan, J. S. Wei, M. Ringnér et al., “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nature Medicine, vol. 7, no. 6, pp. 673–679, 2001. View at Publisher · View at Google Scholar
  14. M. Mørup, L. K. Hansen, and S. M. Arnfred, “Algorithms for sparse non-negative TUCKER (also named HONMF),” Tech. Rep., Technical University of Denmark, Lyngby, Denmark, 2007, View at Google Scholar