Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

In the era of healthcare, and its related research fields, the dimensionality problem of high
dimensional data is a massive challenge as it contains a huge number of variables forming …

Network embedding, aiming to learn the low-dimensional representations of nodes in
networks, is of paramount importance in many real applications. One basic requirement of …

Many real-world datasets are comprised of different representations or views which often
provide information complementary to each other. To integrate information from multiple …

The application of most existing multi-view spectral clustering methods is generally limited
by the following three deficiencies. First, the requirement to post-processing, such as K …

Real data are often with multiple modalities or comingfrom multiple channels, while multi-
view clusteringprovides a natural formulation for generating clustersfrom such data …

Non-negative matrix factorization based multi-view clustering algorithms have shown their
competitiveness among different multi-view clustering algorithms. However, non-negative …

More often than not, a multimedia data described by multiple features, such as color and
shape features, can be naturally decomposed of multi-views. Since multi-views provide …

Multi-view subspace clustering, which aims to partition a set of multi-source data into their
underlying groups, has recently attracted intensive attention from the communities of pattern …

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 …