The Improved Non-negative Matrix Factorization Algorithm for Document Clustering

摘要：

Non-negative Matrix Factorization (NMF) is one latest presented approach for obtaining document clusters, which aimed to provide a minimum error non-negative representation of the term-document matrix. In this paper, we have extended the classical NMF approach by imposing sparseness constraints explicitly. The new model can learn much sparser matrix factorization. Also, an objective function is defined to impose the sparseness constraint, in addition to the nonnegative constraint Experimental results on realworld document datasets show that the proposed method can treat document clustering effectively and efficiently.

作者: Weizhong Zhao Huifang Ma Qing He Zhongzhi Shi

作者单位: College of Information Engineering, Xiangtan University, Hunan 411105 China Key Laboratory of Intell College of Mathematics and Information, Northwest Normal University, Gansu, China Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Aca

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1886-1889

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）