Matrix Recovery Algorithm Based on Normal Vector of Hyperplane in Underdetermined BSS

摘要：

This paper discusses the matrix recovery for sparse source under the k-SCA condition. Here, to estimate the mixing matrix using hyperplane clustering, we propose a new algorithm based on normal vector for hyperplane. The algorithm is an extension of blind separation algorithm using statistical clustering (i.e. kmeans). Compared with the Hough SCA algorithm, we give a method to calculate normal vector for hyperplane, and our algorithm has lower complexity and higher precision. Two examples demonstrates its performance.

作者: Ming Xiao Ronghua Li

作者单位: School of Electrics & Information Engineering South China University of Technology Guangzhou,China 5 School of Electrics & Information Engineering South China University of Technology Guangzhou,China 5

会议类型: 国际会议

会议名称: Fourth International Conference on Impulsive and Hybrid Dynamical Systems(ICIHDS 2007)(第四届国际脉冲和混合动力系统学术会议)

会议地点: 南宁

会议语种:英文

在线出版日期: 2007-07-20（万方平台首次上网日期，不代表论文的发表时间）