Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation

Abstract

The fast growth of Internet and modern technologies has brought data involving objects of multiple types that are related to each other, called as Multi-Type Relational data. Traditional clustering methods for single-type data rarely work well on them, which calls for new clustering techniques, called as high-order co-clustering (HOCC), to deal with the multiple types of data at the same time. A major challenge in developing HOCC methods is how to effectively make use of all available information contained in a multi-type relational data set, including both inter-type and intra-type relationships. Meanwhile, because many real world data sets are often of large sizes, clustering methods with computationally efficient solution algorithms are of great practical interest. In this paper, we first present a general HOCC framework, named as Orthogonal Nonnegative Matrix Tri-factorization (O-NMTF), for simultaneous clustering of multi-type relational data. The proposed O-NMTF approach employs Nonnegative Matrix Tri-Factorization (NMTF) to simultaneously cluster different types of data using the inter-type relationships, and incorporate intra-type information through manifold regularization, where, different from existing works, we emphasize the importance of the orthogonal ties of the factor matrices of NMTF. Based on O-NMTF, we further develop a novel Fast Nonnegative Matrix Tri-Factorization (F-NMTF) approach to deal with large-scale data. Instead of constraining the factor matrices of NMTF to be nonnegative as in existing methods, F-NMTF constrains them to be cluster indicator matrices, a special type of nonnegative matrices. As a result, the optimization problem of the proposed method can be decoupled, which results in sub problems of much smaller sizes requiring much less matrix multiplications, such that our new algorithm scales well to real world data of large sizes. Extensive experimental evaluations have demonstrated the effectiveness of our new approaches.