Adaptive local structure learning for document co-clustering

Abstract

The goal of document co-clustering is to partition textual data sets into groups by utilizing the duality between documents (i.e., data points) and words (i.e., features). That is, the documents can be grouped based on their distribution on words, while words can be grouped based on their distribution on documents. However, traditional co-clustering methods are usually sensitive to the input affinity matrix since they partition the data based on the fixed data graph. To address this limitation, in this paper, based on nonnegative matrix tri-factorization, we propose a new framework of co-clustering with adaptive local structure learning. The proposed unified learning framework performs intrinsic structure learning and tri-factorization (i.e., 3-factor factorization) simultaneously. The intrinsic structure is adaptively learned from the results of tri-factorization, and the factors are reformulated to preserve the refined local structures of the textual data. In this way, the local structure learning and factorization can be mutually improved. Furthermore, considering the duality between documents and words, the proposed framework explores not only the adaptive local structure of the data space, but also the adaptive local structure of the feature space. In order to solve the optimization problem of our method, an efficient iterative updating algorithm is proposed with guaranteed convergence. Experiments on benchmark textual data sets demonstrate the effectiveness of the proposed method.