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	A Sparse Component Analysis Algorithm based on Finite-Mixture-Model Learning
	Jianzhao Qin ; Zhi Wang ; Hanqing Hu ; Jun Cheng ; Xinyu Wu ; Yangsheng Xu
	2007
会议名称	Proceedings of the 2007 IEEE International Conference on Integration Technology
会议地点	Shenzhen, China
英文摘要	In this paper, a finite-mixture-model learning based sparse component analysis (SCA) algorithm is proposed. In this algorithm, a finite-mixture-modellearning 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.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/2093]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Jianzhao Qin,Zhi Wang,Hanqing Hu,et al. A Sparse Component Analysis Algorithm based on Finite-Mixture-Model Learning[C]. 见:Proceedings of the 2007 IEEE International Conference on Integration Technology. Shenzhen, China.

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