AKM3C: Adaptive K-Multiple-Means for Multi-view Clustering

With the popularity of cameras and sensors, massive data are captured from various view angles or modalities, which provide abundant complementary information and also bring great challenges for traditional clustering methods. In this paper, we propose a novel Adaptive K-Multiple-Means for multi-view clustering method (AKM3C). Unlike traditional multi-view K-means methods by grouping samples into C clusters each with a cluster center in every view, the proposed AKM3C employs M(M > C) sub-cluster centers in each view to reveal the sub-cluster structure in the multi-view data thus enhances the clustering performance. Additionally, to distinguish the importance of different views, instead of using empirical weights, AKM3C exploits the multi-view combination weights strategy to assign a weight to each view automatically and thus fuses the complementary information of different views properly to get an optimally shared bipartite graph, on which the Laplacian rank constraint is executed and the final clusters are obtained by directly partitioning. An efficient optimization algorithm proposed with complexity and convergence analysis is used to solve the proposed AKM3C method. The extensive experimental results on eight public datasets show that the proposed AKM3C performs better than state-of-the-art multi-view clustering methods. The code can be downloaded at https://drive.google.com/file/d/1CQ0royrYxKFJdNLnbBQSbDrohtfH71di/view?usp=sharing.