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Volume 31 Issue 2
Dec.  2010
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Li Rong-hua, Yang Zu-yuan, Zhao Min, Xie Sheng-li. SVM Based Underdetermined Blind Source Separation[J]. Journal of Electronics & Information Technology, 2009, 31(2): 319-322. doi: 10.3724/SP.J.1146.2007.01370
Citation: Li Rong-hua, Yang Zu-yuan, Zhao Min, Xie Sheng-li. SVM Based Underdetermined Blind Source Separation[J]. Journal of Electronics & Information Technology, 2009, 31(2): 319-322. doi: 10.3724/SP.J.1146.2007.01370

SVM Based Underdetermined Blind Source Separation

doi: 10.3724/SP.J.1146.2007.01370
  • Received Date: 2007-08-28
  • Rev Recd Date: 2008-04-04
  • Publish Date: 2009-02-19
  • A new sparse measure of signals is proposed in this paper. After the number of efficient sources is estimated, the observations are classified using Support Vector Machine (SVM) trained through samples which are constructed by the direction angles of sources. Each clustering center is obtained based on the sum of samples belong to the same class with different weights which are adjusted adaptively. It gets out of the trap of the initial values which interfere k-mean clustering seriously. Furthermore, the online algorithm is proposed for large scale samples. Simulations show the stability and robustness of the methods.
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