The development of information gathering and extraction technology has led to the
popularity of multi-view data, which enables samples to be seen from numerous …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

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

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 …

Incomplete multi-view clustering (IMVC) analysis, where some views of multi-view data
usually have missing data, has attracted increasing attention. However, existing IMVC …

RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the
fact that depth cues can now be conveniently captured. Existing works often focus on …

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

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the
emergence of multi-view data with missing views in real applications. Recent methods …

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …

Although previous graph-based multi-view clustering (MVC) algorithms have gained
significant progress, most of them are still faced with three limitations. First, they often suffer …