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 …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

Despite significant progress, there remain three limitations to the previous multi-view
clustering algorithms. First, they often suffer from high computational complexity, restricting …

Multi-view clustering divides similar objects into the same class through using the fused
multiview information. Most multi-view clustering methods obtain clustering result by only …

Multiview clustering algorithms have attracted intensive attention and achieved superior
performance in various fields recently. Despite the great success of multiview clustering …

Multi-view subspace clustering aims to discover the hidden subspace structures from
multiple views for robust clustering, and has been attracting considerable attention in recent …

Abstract Recently, Multi-view Representation Learning (MRL) has drawn immense
attentions in the analysis of multi-source data and ubiquitously employed across different …

Multi-view bipartite graph clustering methods select a few representative anchors and then
establish a connection with original samples to generate the bipartite graphs for clustering …

Multi-view semi-supervised classification is a typical task to classify data using a small
amount of supervised information, which has attracted a lot of attention from researchers in …

Multi-view non-negative matrix factorization (NMF) provides a reliable method to analyze
multiple views of data for low-dimensional representation. A variety of multi-view learning …