Connectionists: NMF and SCA papers and MATLAB software NMFLAB

[A. Cichocki]

Dear List Members:


I would like to bring to your attention  our 
recently updated papers and reports about 
NMF (non-negative matrix factorization) 
and SCA (Sparse Component Analysis) for 
BSS (Blind and Semi-blind Source Separation) available at:


http://www.bsp.brain.riken.jp/%7Ecia/recent.html#nmf
http://www.bsp.brain.riken.jp/~cia/recent.html#sca
http://www.bsp.brain.riken.jp/~cia/

We release also soon new free MATLAB toolboxes: NMFLAB  and SCALAB.

I would be grateful for any critical comments or suggestions.

Best Wishes,

Andrzej Cichocki
===============
Laboratory for Advanced Brain Signal Processing
Riken, Brain Science Institute, JAPAN
Wako Shi, Saitama 351-0198



List of selected new papers and reports about sparse NMF and SCA:

NMF 

   1. A. Cichocki, R. Zdunek,  and  S.  Amari, "Csiszar's Divergences 
      for Non-Negative Matrix Factorization: Family of New 
      Algorithms",  6th International Conference on Independent
      Component Analysis and Blind Signal Separation, Charleston SC,
      USA, March 5-8, 2006. [.pdf
      <http://www.bsp.brain.riken.jp/publications/2006/ICA-Ci-Zd-Amari8-12.pdf>]
   2. A. Cichocki,  S. Amari, and R. Zdunek, "Extended SMART Algorithms
      for Non-Negative Matrix Factorization",  Eighth International
      Conference on Artificial Intelligence and Soft Computing, ICAISC,
      Zakopane, Poland, 25-29 June, 2006. [.pdf
      <http://www.bsp.brain.riken.jp/publications/2006/ICAISC-06.pdf>]
   3. R. Zdunek,  and  A. Cichocki, "Non-Negative Matrix Factorization
      with Quasi-Newton Optimization", Eighth International Conference
      on Artificial Intelligence and Soft Computing, ICAISC, Zakopane,
      Poland, 25-29 June, 2006. [.pdf
      <http://www.bsp.brain.riken.jp/publications/2006/ICAISCP_Newton.pdf>]

SCA

   1. P. G. Georgiev, F. Theis, and A. Cichocki, "Sparse component
      analysis and blind source separation of underdetermined mixtures",
      IEEE Transactions on Neural Networks, July 2005, Vol. 16, No.4,
      pp. 992-996. [.pdf
      <http://www.bsp.brain.riken.jp/publications/2005/TNN-2005.pdf>]
   2. P. G. Georgiev, F. Theis, and A. Cichocki, "Optimization
      algorithms for sparse representations and applications", Chapter
      in the book Mulitscale Optimization Methods, Ed. Pardalos, 2005.
      [.pdf
      <http://www.bsp.brain.riken.jp/publications/2005/Ge_Th_Ci_MultiscaleOptimization_volume.pdf>]
   3. Y. Li, S. Amari, A. Cichocki,  D. W. C. Ho and S. Xie:
      "Underdetermined Blind Source Separation Based on Sparse
      Representation", IEEE Transactions on Signal Processing, Vol. 54,
      No.2, 2006 (in print).  [.pdf
      <http://www.bsp.brain.riken.jp/publications/2006/54tsp02-li-proof.pdf>]

   4. Y. Li, A. Cichocki, and S. Amari, "Blind estimation of channel
      parameters and source components for EEG signals: A sparse
      factorization approach, IEEE Transactions on Neural Networks, 
      2006, (accepted for publication) [ draft version pdf
      <http://www.bsp.brain.riken.jp/publications/2005/IEEETranNN-2005.pdf>
      ]
   5. F. J. Theis, P. G. Georgiev, and A. Cichocki, "Robust overcomplete
      matrix recovery for sparse sources using a generalized Hough
      transform," in Proceedings of 12th European Symposium on
      Artificial Neural Networks (ESANN2004), (Bruges, Belgium),
      pp. 343-348, Apr. 2004. [.pdf
      <http://www.bsp.brain.riken.jp/publications/2004/robustm1BMMR_ESANN_revised.pdf>]