A Graphbased Image Clustering Method Using Mutual Information Maximization In this paper, we present a novel image clustering method that converts a typical image clustering problem into a graph node classification task, given only unlabelled data samples. Graph convolutional network is utilized to perform graph representation encoding. Unlike traditional clustering approaches relying on handcrafted criteria, our method learns to cluster graphstructured data by maximizing mutual information between the globalgraph representation and localgraph representations. The learnt graph embeddings can preserve global information in all locations of localgraphs such as pseudo labels for image clustering tasks. To test the generalization and robustness of the proposed method, we conduct experiments on two different benchmarks, such as fashion classification and finegrained object classification. The preliminary experiment results show that the proposed method outperforms all the baselines.

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.