Title :
The Estimation of Mixing Matrix Based on Bernoulli-Gaussian Model
Author :
Wen, Jiechang ; Wang, Taowen
Author_Institution :
Fac. of Appl. Math., Guangdong Univ. of Technol., Guangzhou, China
Abstract :
Based on Bernoulli-Gaussian model and the sparseness of source signals, a new algorithm for estimating the mixing matrix is proposed in this paper. It estimates the mixing matrix by searching the cluster points which are found through the density of points in the region. In order to enhance the precision of the algorithm, the cost function is constructed to search the cluster points. The last simulations show the good performance of the proposed algorithm.
Keywords :
blind source separation; pattern clustering; sparse matrices; Bernoulli-Gaussian model; algorithm precision enhancement; cluster point searching; cost function; mixing matrix estimation; source signal sparseness; Blind source separation; Clustering algorithms; Equations; Estimation; Mathematical model; Signal processing algorithms; Sparse matrices; Bernoulli-Gaussian model; cluster point; sparse component analysis; underdetermined mixture;
Conference_Titel :
Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
Conference_Location :
Hainan
Print_ISBN :
978-1-4577-2008-6
DOI :
10.1109/CIS.2011.307
