Automated Graph Regularized Projective Nonnegative Matrix Factorization for Document Clustering

In this paper, a novel projective nonnegative matrix factorization (PNMF) method for enhancing the clustering performance is presented, called automated graph regularized projective nonnegative matrix factorization (AGPNMF). The idea of AGPNMF is to extend the original PNMF by incorporating the automated graph regularized constraint into the PNMF decomposition. The key advantage of this approach is that AGPNMF simultaneously finds graph weights matrix and dimensionality reduction of data. AGPNMF seeks to extract the data representation space that preserves the local geometry structure. This character makes AGPNMF more intuitive and more powerful than the original method for clustering tasks. The kernel trick is used to extend AGPNMF model related to the input space by some nonlinear map. The proposed method has been applied to the problem of document clustering using the well-known Reuters-21578, TDT2, and SECTOR data sets. Our experimental evaluations show that the proposed method enhances the performance of PNMF for document clustering.