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| SVM Based Underdetermined Blind Source Separation |
| Li Rong-hua; Yang Zu-yuan; Zhao Min;Xie Sheng-li |
| School of Electronics & Information Engineering, South China University of Technology, Guangzhou 510640, China |
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Abstract 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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Received: 28 August 2007
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2009, Vol. 31 
