Multi-view clustering, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise...

In many clustering applications, real world data are often collected from multiple sources or with features from multiple channels. Thus, multi-view clustering has attracted much attention during the past few years. It is noteworthy that in many situations, in addition to the data samples, there is some side information describing the relation between instances, such as must-links and cannot-li...

Exploiting the information from multiple views can improve clustering accuracy. However, most existing multi-view clustering algorithms are nonconvex and are thus prone to becoming stuck into bad local minima, especially when there are outliers and missing data. To overcome this problem, we present a new multi-view self-paced learning (MSPL) algorithm for clustering, that learns the multi-view ...

In multi-view clustering, different views may have different confidence levels when learning a consensus representation. Existing methods usually address this by assigning distinctive weights to different views. However, due to noisy nature of realworld applications, the confidence levels of samples in the same viewmay also vary. Thus considering a unified weight for a view may lead to suboptim...

With the increase of multi-view graph data, clustering (MVGC) that can discover hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to given graphs, especially influenced by low quality i.e., they tend be limited homophily assumption. However, widespread real-world data hardly satisfy This gap limits performance e...