Image Clustering Based on Graph Regularized Robust Principal Component Analysis. Image clustering has become one of the most popular themes in web based recommendation system. In this study, we propose a novel image clustering algorithm referred as graph regularized robust principal component analysis (GRPCA). Unlike existing spectral rotation or kmeans method, no discretization step is required in our proposed method by imposing nonnegative constraint explicitly. Besides, in GRPCA an affinity graph is constructed to encode the locality manifold information, and the global graph structure is respected by applying matrix factorization. The proposed method is robust to model selection that is more appealing for real unsupervised applications. Extensive experiments on three publicly available image datasets demonstrate the effectiveness of our algorithm.

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.