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 …

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 …

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

Multi-view clustering (MVC) aims to fuse the information among multiple views to achieve
effective clustering. Many MVC algorithms based on semi-nonnegative matrix factorization …

Multi-view subspace clustering targets at clustering data lying in a union of low-dimensional
subspaces. Generally, an n X n affinity graph is constructed, on which spectral clustering is …

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

Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise
similarities and therefore reduce the complexity of graph methods. Although widely applied …