A Sparse Component Analysis Algorithm Based on Finite-Mixture-Model Learning
Jianzhao Qin, Zhi Wang, Hanqing Hu, Jun Sheng Cheng, Xinyu Wu, Yangsheng Xu · 2007
In this paper, a finite-mixture-model learning bused sparse component analysis (SCA) algorithm is proposed. In this algorithm, a finite-mixture-model learning method is applied for estimating the mixing matrix for SCA. The main advantage of this method is the ability of selecting the number of sources and measuring reliability of the columns of the estimated mixing matrix. That is, it can give us a probability measurement of the recovered sources, which help us to determine which recovered sources are more reliable and significant. The simulation results show the effectiveness of this algorithm.