Sparse non-negative matrix factorization with generalized kullback-leibler divergence

doi:10.1007/978-3-319-46257-8_38

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 自动推理与认知研究中心

	Sparse non-negative matrix factorization with generalized kullback-leibler divergence
	Chen, Jingwei1 ; Feng, Yong1 ; Liu, Yang 2; Tang, Bing 3; Wu, Wenyuan1
	2016
会议日期	October 12, 2016 - October 14, 2016
会议地点	Yangzhou, China
DOI	10.1007/978-3-319-46257-8_38
页码	353-360
通讯作者	Liu, Yang (ly1246@qq.com)
英文摘要	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.
会议录	17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
语种	英语
电子版国际标准刊号	16113349
ISSN号	03029743
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/4631]
专题	自动推理与认知研究中心
作者单位	1.Chongqing Key Laboratory of Automated Reasoning and Cognition, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing; 400714, China; 2.College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing; 400074, China; 3.School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan; 411201, China
推荐引用方式 GB/T 7714	Chen, Jingwei,Feng, Yong,Liu, Yang,et al. Sparse non-negative matrix factorization with generalized kullback-leibler divergence[C]. 见:. Yangzhou, China. October 12, 2016 - October 14, 2016.