Convex-Nonnegative Matrix Factorization with Structure Constraints

被引：0

作者：

Pei, Xiaobing ^{[1
]}

Wu, Tao ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Software, Wuhan 430074, Hubei, Peoples R China

来源：

2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) | 2013年

关键词：

Convex-Nonnegative matrix factorization; clustering; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nonnegative Matrix Factorization (NMF) is of great use in finding basis information of non-negative data. In this paper, a novel Convex-NMF (CNMF) method is presented, called Structure Constrained Convex-Nonnegative Matrix Factorization (SCNMF). The idea of SCNMF is to extend the original Convex-NMF by incorporating the structure constraints into the Convex-NMF decomposition. The SCNMF seeks to extract the representation space that preserves the geometry structure. Finally, our experiment results are presented.

引用

页码：456 / 460

页数：5

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