Orthogonal Nonnegative Matrix Tri-factorization for Semi-supervised Document Co-clustering

Semi-supervised clustering is often viewed as using labeled data to aid the clustering process. However, existing algorithms fail to consider dual constraints between data points (e.g. documents) and features (e.g. words). To address this problem, in this paper, we propose a novel semi-supervised document co-clustering model OSS-NMF via orthogonal nonnegative matrix tri-factorization. Our model incorporates prior knowledge both on document and word side to aid the new wordcategory and document-cluster matrices construction. Besides, we prove the correctness and convergence of our model to demonstrate its mathematical rigorous. Our experimental evaluations show that the proposed document clustering model presents remarkable performance improvements with certain constraints.