Nonnegative Matrix Factorization for Independent Component Analysis

摘要：

In this paper, we develop a new algorithm with improved efficiency for nonnegative independent component analysis. This algorithm utilizes Kullback-Leibler divergence to generate nonnegative matrix factorization of the observation vec-tors.During the factorization, by pre-whitening the observations and orthonormalizing the mixing matrix, the independent compo-nents of sources are obtained. In the simulation, we successfully apply the developed algorithm to blind source separation of three images where sources are statistically independent.

作者: Shangming Yang Zhang Yi

作者单位: Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 P.R.China

会议类型: 国际会议

会议名称: 2007年通信、电路与系统国际会议(2007 International Conference on Communications,Circuits and Systems Proceedings)

会议地点: 日本福冈

会议语种:英文

在线出版日期: 2007-07-11（万方平台首次上网日期，不代表论文的发表时间）