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 …

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 …

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of
high-dimensional data. Most of the existing multi-view clustering methods are based on non …

A panoply of multi-view clustering algorithms has been developed to deal with prevalent
multi-view data. Among them, spectral clustering-based methods have drawn much attention …

Spectral clustering converts the data clustering problem to the graph cut problem. It is based
on graph theory. Due to the reliable theoretical basis and good clustering performance …

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel
learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids …

The goal of multi-view clustering is to partition samples into different subsets according to
their diverse features. Previous multi-view clustering methods mainly exist two forms: multi …

Similarity matrix is of vital importance for graph-based multi-view clustering models, which
can depict the nonlinear structure information among samples. However, most existing …

Non-negative matrix factorization (NMF) has attracted sustaining attention in multi-view
clustering, because of its ability of processing high-dimensional data. In order to learn the …

Low-rank multi-view subspace clustering has recently attracted increasing attention in the
multi-view learning research. Despite significant progress, most existing approaches still …