Multi-view clustering (MVC) has attracted more and more attention in the recent few years by
making full use of complementary and consensus information between multiple views to …

With the widespread deployment of sensors and the Internet-of-Things, multi-view data has
become more common and publicly available. Compared to traditional data that describes …

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

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

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

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 …

Multi-view subspace clustering algorithms have recently been developed to process multi-
view dataset clustering by accurately depicting the essential characteristics of multi-view …

Multi-view clustering (MVC) methods aim to exploit consistent and complementary
information among each view and achieve encouraging performance improvement than …

Sparsity-constrained optimization problems are common in machine learning, such as
sparse coding, low-rank minimization and compressive sensing. However, most of previous …

Multi-view subspace clustering aims at separating data points into multiple underlying
subspaces according to their multi-view features. Existing low-rank tensor representation …