Abstract
A novel sparse measure of signal is proposed and the efficient number of sources is estimated by the best confidence limit in this work. The observations are classified by SVM trained through samples which are constructed by direction angle of sources. And columns of the mixing matrix corresponding to clustering centers of each class are 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 quite a lot. Furthermore, the online algorithm for estimating basis matrix is proposed for large scale samples. The shortest path method is used to recover the source signals after estimating the mixing matrix. The favorable simulations show the stability and robustness of the algorithms.
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Zuyuan, Y., Shiguang, L., Caiyun, C. (2007). Underdetermined Blind Source Separation Using SVM. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_98
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DOI: https://doi.org/10.1007/978-3-540-72395-0_98
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