Non-Linear Fusion for Self-Paced Multi-View Clustering

With the advance of the multi-media and multi-modal data, multi-view clustering (MVC) has drawn increasing attentions recently. In this field, one of the most crucial challenges is that the characteristics and qualities of different views usually vary extensively. Therefore, it is essential for MVC methods to find an effective approach that handles the diversity of multiple views appropriately. To this end, a series of MVC methods focusing on how to integrate the loss from each view have been proposed in the past few years. Among these methods, the mainstream idea is assigning weights to each view and then combining them linearly. In this paper, inspired by the effectiveness of non-linear combination in instance learning and the auto-weighted approaches, we propose Non-Linear Fusion for Self-Paced Multi-View Clustering (NSMVC), which is totally different from the the conventional linear-weighting algorithms. In NSMVC, we directly assign different exponents to different views according to their qualities. By this way, the negative impact from the corrupt views can be significantly reduced. Meanwhile, to address the non-convex issue of the MVC model, we further define a novel regularizer-free modality of Self-Paced Learning (SPL), which fits the proposed non-linear model perfectly. Experimental results on various real-world data sets demonstrate the effectiveness of the proposed method.