Autoweighted Multiview Coclustering via Fast Matrix Factorization Abstract   Multiview clustering is a hot research topic in machine learning and pattern recognition, however, it remains high computational complexity when clustering multiview data sets. Although a number of approaches have been proposed to accelerate the computational efficiency, most of them do not consider the data duality between features and samples. In this paper, we propose a novel coclustering approach termed as Fast Multiview Bilateral Kmeansxc2xa0(FMVBKM), which can implement clustering task on row and column of the input data matrix, simultaneously. Specifically, FMVBKM applies the relaxed Kmeans clustering technique to multiview data clustering. In addition, to decrease information loss in matrix factorization, we further introduce a new coclustering method named as Fast Multiview Matrix TriFactorizationxc2xa0(FMVMTF). Extensive experimental results on six benchmark data sets show that the proposed two approaches not only have comparable clustering performance but also present the high computational efficiency, in comparison with stateoftheart multiview clustering methods.

Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multiview Clustering Graph Learning has emerged as a promising technique for multiview clustering, and has recently attracted lots of attention due to its capability of adaptively learning a unified and probably better graph from multiple views. However, the existing multiview graph learning methods mostly focus on the multiview consistency, but neglect the potential multiview inconsistency (which may be incurred by noise, corruptions, or viewspecific characteristics). To address this, this paper presents a new graph learningbased multiview clustering approach, which for the first time, to our knowledge, simultaneously and explicitly formulates the multiview consistency and the multiview inconsistency in a unified optimization model. To solve this model, a new alternating optimization scheme is designed, where the consistent and inconsistent parts of each singleview graph as well as the unified graph that fuses the consistent parts of all views can be iteratively learned. It is noteworthy that our multiview graph learning model is applicable to both similarity graphs and dissimilarity graphs, leading to two graph fusionbased variants, namely, distance (dissimilarity) graph fusion and similarity graph fusion. Experiments on various multiview datasets demonstrate the superiority of our approach. The MATLAB source code is available at https:github.comyouweiliangConsistentGraphLearning.