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 …

With advances in information acquisition technologies, multi-view data become ubiquitous.
Multi-view learning has thus become more and more popular in machine learning and data …

Multi-view clustering, which exploits the multi-view information to partition data into their
clusters, has attracted intense attention. However, most existing methods directly learn a …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the
past few years. Researchers manage to boost clustering accuracy from different points of …

Graph clustering aims to discover community structures in networks, the task being
fundamentally challenging mainly because the topology structure and the content of the …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

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 …

Unsupervised hashing methods have attracted widespread attention with the explosive
growth of large-scale data, which can greatly reduce storage and computation by learning …