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 …

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 …

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

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 …

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 …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the
past few years. Researchers manage to boost clustering accuracy from different points of …

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across
the world. Due to the large number of infected patients and heavy labor for doctors …