High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Multi-view subspace clustering has attracted intensive attention to effectively fuse multi-view
information by exploring appropriate graph structures. Although existing works have made …

In artificial intelligence systems, a question on how to express the uncertainty in knowledge
remains an open issue. The negation scheme provides a new perspective to solve this …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

Deep graph clustering, which aims to reveal the underlying graph structure and divide the
nodes into different groups, has attracted intensive attention in recent years. However, we …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

Multi-view subspace clustering has received widespread attention to effectively fuse multi-
view information among multimedia applications. Considering that most existing …

Despite significant progress, there remain three limitations to the previous multi-view
clustering algorithms. First, they often suffer from high computational complexity, restricting …

Multi-view clustering has received increasing attention due to its effectiveness in fusing
complementary information without manual annotations. Most previous methods hold the …