Robust co-clustering via dual local learning and high-order matrix factorization

Co-clustering is to group features and samples simultaneously and has received increasing attention in data mining and machine learning, particularly in text document categorization and gene expression. In this paper, two effective co-clustering algorithms are proposed to exploit the joint advantages of local learning and matrix factorization. First, the co-clustering problem is formulated as a form of matrix tri-factorization which embeds local structure learning and orthogonality constraints for clustering indicators. Using high-order matrix factorization, an effective algorithm is proposed for co-clustering problems and its convergence is proved. Second, symmetric co-clustering problems are studied, where the sample affinity matrix serves as the input matrix. Analogous high-order matrix factorization is used to develop an effective convergent algorithm for that problem. Finally, the two proposed algorithms are validated in eight publicly available real-world datasets from machine learning repository. Extensive experiments demonstrate that the proposed algorithms achieve competitive performance over existing state-of-the-art co-clustering methods in all tested datasets.