Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 2, Pages 343-352Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2626311
Keywords
Concept factorization (CF); document clustering
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Funding
- Fundamental Research Funds for the Central Universities, HUST [2015TS110]
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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.
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