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	Sparse non-negative matrix factorization with generalized kullback-leibler divergence
	Chen, Jingwei1; Feng, Yong1; Liu, Yang2; Tang, Bing3; Wu, Wenyuan1
	2016
摘要	Non-negative Matrix Factorization (NMF), especially with sparseness constraints, plays a critically important role in data engineering and machine learning. Hoyer (2004) presented an algorithm to compute NMF with exact sparseness constraints. The exact sparseness constraints depends on a projection operator. In the present work, we first give a very simple counterexample, for which the projection operator of the Hoyer (2004) algorithm fails. After analysing the reason geometrically, we fix this bug by adding some random terms and show that the fixed one works correctly. Based on the fixed projection operator, we propose another sparse NMF algorithm aiming at optimizing the generalized Kullback-Leibler divergence, hence named SNMF-GKLD. Experimental results show that SNMF-GKLD not only has similar effects with Hoyer (2004) on the same data sets, but is also efficient. © Springer International Publishing AG 2016.
语种	英语
DOI	10.1007/978-3-319-46257-8_38
会议(录)名称	17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
页码	353-360
通讯作者	Liu, Yang (ly1246@qq.com)
收录类别	EI
会议地点	Yangzhou, China
会议日期	October 12, 2016 - October 14, 2016