Blind Recovery of Mixing Matrix with Sparse Sources Based on Improved K-means Clustering and Hough Transform
Xiyuan Peng · Dianzi xuebao · 2009
Blind mixing matrix recovery is one of the most important steps in blind separation of sparse sources,which impacts significantly on the recovery accuracy of source signals.A novel improved K-means clustering algorithm is proposed based on differential evolution,to avoid the partial convergence problem of the K-means algorithm.The proposed algorithm is applied to allocate the sparse mixture data to several clusters,thus guaranteeing the robustness of the clustering.Then the cluster centers are amended through Hough transform to recover the mixing matrix.Experimental results show that the proposed mixing matrix recovery algorithm has advantages of high robustness and accuracy compared with conventional algorithms.