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 …

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 …

Multiview clustering has gained increasing attention recently due to its ability to deal with
multiple sources (views) data and explore complementary information between different …

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 …

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 …

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