Nonnegative Matrix Factorization with Maximum Self-Information on Basis Components

摘要：

In this paper, we propose a novel method, called nonnegative matrix factorization with maximum self-information on basis components (NMFMSI), for learning informative, spatially localized and parts-based subspace representations of visual patterns. In addition to the nonnegativity constraint in the standard nonnegative matrix factorization model, a new objective function is defined to impose maximum, self-information and smoothness constraints on the basis components and the encoding vectors, respectively. NMFMSI yields a set of basis features which not only allows an additive representation of data but also contains more information about the data. The self-information of a basis feature is closely related to its probability density. By using the Oja rules, an algorithm is presented for learning the nonnegative solutions of NMFMSI. Experimental results on the swimmer dataset and ORL face database demonstrate the advantages of NMFMSI.

作者: Ji-Yuan Pan Jiang-She Zhang

作者单位: Faculty of Science and State Key Laboratory for Manufacturing Systems Engineering Xian Jiaotong University, Xian Shaanxi Province, 710049, China

会议类型: 国际会议

会议名称: 2011 6th Joint International Information Technology and Artificial Intelligence Conference(2011年第六届IEEE联合国际信息技术与人工智能会议 IEEE ITAIC 2011)

会议地点: 重庆

会议语种:英文

页码: 128-132

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）