Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

2014/10/03 by Le Li, Jianjun Yang, Li, Le +7
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Text and Document Classification Technologies #cs.IR

paper · pdf · doi:10.48550/arxiv.1410.0993

International Conference of Machine Learning and Cybernetics (ICMLC) 2014

arxiv created 2014/10/03 · openalex publication_date 2014/10/03 · arxiv updated 2014/10/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the l₂ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.

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