Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

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 …

This paper studies clustering of multi-view data, known as multi-view clustering. Among
existing multi-view clustering methods, one representative category of methods is the graph …

Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …

Real data are often collected from multiple channels or comprised of different
representations (ie, views). Multi-view learning provides an elegant way to analyze the multi …

Datasets are often collected from different resources or comprised of multiple
representations (ie, views). Multi-view clustering aims to analyze the multi-view data in an …

Clustering is an important topic in machine learning and data mining. Recently, deep
clustering, which learns feature representations for clustering tasks using deep neural …

Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning
in kernel space has shown impressive performance on a number of benchmark data sets …

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 …

The rapid growth of the number of data brings great challenges to clustering, especially the
introduction of multi-view data, which collected from multiple sources or represented by …