Concept Factorization With Adaptive Neighbors for Document Clustering

被引:41
作者
Pei, Xiaobing [1 ]
Chen, Chuanbo [1 ]
Gong, Weihua [2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Software, Wuhan 430074, Hubei, Peoples R China
[2] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China
关键词
Concept factorization (CF); document clustering; NONNEGATIVE MATRIX FACTORIZATION; DIMENSIONALITY REDUCTION; IMAGE REPRESENTATION; QUANTIZATION; NUMBER;
D O I
10.1109/TNNLS.2016.2626311
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a novel concept factorization (CF) method, called CF with adaptive neighbors (CFANs), is proposed. The idea of CFAN is to integrate an ANs regularization constraint into the CF decomposition. The goal of CFAN is to extract the representation space that maintains geometrical neighborhood structure of the data. Similar to the existing graph-regularized CF, CFAN builds a neighbor graph weights matrix. The key difference is that the CFAN performs dimensionality reduction and finds the neighbor graph weights matrix simultaneously. An efficient algorithm is also derived to solve the proposed problem. We apply the proposed method to the problem of document clustering on the 20 Newsgroups, Reuters-21578, and TDT2 document data sets. Our experiments demonstrate the effectiveness of the method.
引用
收藏
页码:343 / 352
页数:10
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