Matrix estimation based on normal vector of hyperplane in sparse component analysis

Authors:
Feng Gao;Gongxian Sun;Ming Xiao;Jun Lv
Affiliations:
School of Electric & Information Engineering, South China University of Technology, Guangzhou, China;School of Electric & Information Engineering, South China University of Technology, Guangzhou, China;,School of Electric & Information Engineering, South China University of Technology, Guangzhou, China;School of Electric & Information Engineering, South China University of Technology, Guangzhou, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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Abstract

This paper discusses the matrix estimation for sparse component analysis 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. Compared with the Hough SCA algorithm, we give a method to calculate normal vector for hyperplane, and the algorithm has lower complexity and higher precision. Two examples demonstrates its performance.