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 …

In real-world applications, it is common that only a portion of data is aligned across views
due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called …

Although demonstrating great success, previous multi-view unsupervised feature selection
(MV-UFS) methods often construct a view-specific similarity graph and characterize the local …

Graph learning has emerged as a promising technique for multi-view clustering due to its
efficiency of learning a unified graph from multiple views. Previous multi-view graph learning …

Multi-view clustering has attracted increasing attentions recently by utilizing information from
multiple views. However, existing multi-view clustering methods are either with high …

Deep clustering gradually becomes an important branch in unsupervised learning methods.
However, current approaches hardly take into consideration the semantic sample …

Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …

Multi-view representation learning (MvRL) aims to learn a consensus representation from
diverse sources or domains to facilitate downstream tasks such as clustering, retrieval, and …