A new geometric approach to blind source separation of bounded sources

Abstract Based on the minimum-range approach, a new geometric approach is proposed to deal with blind source separation in this paper. The new approach is the batch mode of the original minimum-range approach. Compared with the original approach, the optimization algorithm of the proposed approach needs no parameters and is more efficient and reliable. In addition, the extension of minimum-range-based approaches is discussed. The simulations show the efficiency of the proposed approach.

[1]  Michel Verleysen,et al.  A Minimum-Range Approach to Blind Extraction of Bounded Sources , 2007, IEEE Transactions on Neural Networks.

[2]  Jacek M. Zurada,et al.  Blind extraction of singularly mixed source signals , 2000, IEEE Trans. Neural Networks Learn. Syst..

[3]  Noboru Ohnishi,et al.  Blind multiuser separation of instantaneous mixture algorithm based on geometrical concepts , 2002, Signal Process..

[4]  Kazuyoshi Itoh,et al.  Independent component analysis by transforming a scatter diagram of mixtures of signals , 2000 .

[5]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[6]  A. Prieto,et al.  Geometric approach for blind separation of signals , 1997 .

[7]  James V. Stone Blind deconvolution using temporal predictability , 2002, Neurocomputing.

[8]  Daniel W. C. Ho,et al.  Underdetermined blind source separation based on sparse representation , 2006, IEEE Transactions on Signal Processing.

[9]  Zhaoshui He,et al.  Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[11]  Zhaoshui He,et al.  A Note on Stone's Conjecture of Blind Signal Separation , 2005, Neural Computation.

[12]  Liqing Zhang,et al.  A Note on Lewicki-Sejnowski Gradient for Learning Overcomplete Representations , 2008, Neural Computation.

[13]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[14]  Alper T. Erdogan,et al.  Globally Convergent Deflationary Instantaneous Blind Source Separation Algorithm for Digital Communication Signals , 2007, IEEE Transactions on Signal Processing.

[15]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[16]  Alper T. Erdogan,et al.  A simple geometric blind source separation method for bounded magnitude sources , 2006, IEEE Transactions on Signal Processing.

[17]  Yuanqing Li,et al.  Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.