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 …

Deep multi-view subspace clustering has achieved promising performance compared with
other multi-view clustering. However, existing deep multi-view subspace clustering only …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

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 …

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic
properties of different views are diverse, even incompatible, few approaches implement …

Clustering analysis is widely used as a pre-process to discover the data distribution for
resampling. Existing clustering-based resampling methods mostly run unsupervised …

Distinguishing the importance of views plays a key role in multi-view learning as each view
often contributes differently to a specific task. However, existing strategies generally attach …

The multi-view learning is a fundamental problem in the multimedia analysis. However, most
existing multi-view learning methods need to calculate a similarity matrix for each view. This …

Nowadays, multi-dimensional data (tensor data) have shown their capability of preserving
multilinear structures. Due to the measuring error or other non-human factors, these data …

Contemporary datasets are often comprised of multiple views of data, which provide
complete and complementary information in different views, and multi-view clustering is one …