Robust multi-view fuzzy clustering via softmin

Hongyuan Zhang, Rui Zhang, Xuelong Li, Yueshen Xu

Abstract: Multi-view clustering, which utilizes the ample information provided by multiple sources to obtain better performance, has attracted much attention. However, existing clustering algorithms either have no ability to offer confidence for each assignment or suffer from the disturbance of outliers. To address these problems, in this paper, we propose a novel multi-view fuzzy clustering method via transferring softmin to fuzzy models. To obtain fuzzy assignments, we utilize the softmin with temperature and further develop an efficient algorithm to solve the non-convex problem approximately. We also show another explanation for the algorithm from the aspect of the prior distribution of various views. Besides, we design a scalable robust loss function, which interpolates between ℓ2-norm and the squared ℓ2-norm, to enhance the robustness to outliers. Extensive experiments show the superiority of our model under different clustering metrics.