Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …

Unsupervised visible-infrared person re-identification is a challenging task due to the large
modality gap and the unavailability of cross-modality correspondences. Cross-modality …

Multi-view clustering (MVC) is a popular technique for improving clustering performance
using various data sources. However, existing methods primarily focus on acquiring …

The success of existing multi-view clustering relies on the assumption of sample integrity
across multiple views. However, in real-world scenarios, samples of multi-view are partially …

Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …

Multi-view clustering (MVC), which effectively fuses information from multiple views for better
performance, has received increasing attention. Most existing MVC methods assume that …

Multi-view bipartite graph clustering methods select a few representative anchors and then
establish a connection with original samples to generate the bipartite graphs for clustering …

Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the
complementary information conveyed by multiple views of objects to be clustered. Recently …

Sample labeling is the most primary and fundamental step of semi-supervised learning. In
literature, most existing methods randomly label samples with a given ratio, but achieve …

Anchor based pair-wised multi-view clustering often assumes multi-view data are paired,
and has demonstrated significant advancements in recent years. However, this presumption …