Graph-based methods have hitherto been used to pursue the coherent patterns of data due its ease implementation and efficiency. These increasingly applied in multi-view learning achieved promising performance various clustering tasks. However, despite their noticeable empirical success, existing graph-based may still suffer suboptimal solution considering that can be very complicated raw featur...

In multi-view clustering, an eigen-decomposition of the Laplacian matrix graph is usually necessary. This leads to a significant increase in time cost and also requires post-processing such as $k$ -means. addition, some methods requir...

Graph-based multi-view clustering (G-MVC) constructs a graphical representation of each view and then fuses them to unified graph for clustering. Though demonstrating promising performance in various applications, we observe that their formulations are usually non-convex, leading local optimum. In this paper, propose novel MVC algorithm termed robust graph-based (RG-MVC) address issue. particul...

Incomplete multi-view clustering, which aims to solve the clustering problem on incomplete data with partial view missing, has received more and attention in recent years. Although numerous methods have been developed, most of either cannot flexibly handle arbitrary missing views or do not consider negative factor information imbalance among views. Moreover, some fully explore local structure a...