Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization
This is an incremental improvement for document clustering, addressing nonlinear data issues in existing NMF methods.
The paper tackled document clustering by proposing a new non-negative matrix factorization method that maximizes correntropy to handle nonlinear cases, and it showed superior performance over other NMF variants on Reuters21578 and TDT2 datasets.
Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ 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.