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2007
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Pairwise Constraints-Guided Non-negative Matrix Factorization for Document Clustering

9 years 7 months ago
Pairwise Constraints-Guided Non-negative Matrix Factorization for Document Clustering
Nonnegative Matrix Factorization (NMF) has been proven to be effective in text mining. However, since NMF is a well-known unsupervised components analysis technique, the existing NMF method can not deal with prior constraints, which are beneficial to clustering or classification tasks. In this paper, we address the text clustering problem via a novel strategy, called Pairwise Constraintsguided Non-negative Matrix Factorization (PCNMF for short). Differing from the traditional NMF method, the proposed method can capture the available abundance prior constraints in original space, which result in more effective for clustering or information retrieval. Therefore, PCNMF enforces the discriminative capability in the reduced space. Utilizing the appropriate transformation, PCNMF represents as a new optimization problem, which can be efficiently solved by an iterative approach. The cluster membership of each document can be easily determined as the standard NMF. Empirical studies based on...
Yujiu Yang, Bao-Gang Hu
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where WEBI
Authors Yujiu Yang, Bao-Gang Hu
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