Multi-view clustering (MVC) has attracted more and more attention in the recent few years by
making full use of complementary and consensus information between multiple views to …

The success of existing multi-view clustering methods heavily relies on the assumption of
view consistency and instance completeness, referred to as the complete information …

Multi-view clustering, which exploits the multi-view information to partition data into their
clusters, has attracted intense attention. However, most existing methods directly learn a …

Aligning distributions of view representations is a core component of today's state of the art
models for deep multi-view clustering. However, we identify several drawbacks with naively …

Multi-view clustering divides similar objects into the same class through using the fused
multiview information. Most multi-view clustering methods obtain clustering result by only …

The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …

Although multi-view learning has made significant progress over the past few decades, it is
still challenging due to the difficulty in modeling complex correlations among different views …

Multi-view subspace clustering algorithms have recently been developed to process multi-
view dataset clustering by accurately depicting the essential characteristics of multi-view …

Graph based multi-view clustering has been paid great attention by exploring the
neighborhood relationship among data points from multiple views. Though achieving great …

Despite multi-view learning progressed fast in past decades, it is still challenging due to the
difficulty in modeling complex correlation among different views, especially under the …