Graph and subspace clustering methods have become the mainstream of multi-view
clustering due to their promising performance. However,(1) since graph clustering methods …

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 …

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 …

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all
the previous studies assume that views are complete. However, in reality, it is often the case …

Multi-view subspace clustering aims at separating data points into multiple underlying
subspaces according to their multi-view features. Existing low-rank tensor representation …

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …

Multi-view clustering (MVC) enhances the clustering performance of data by combining
correlation information from different views. However, most existing MVC approaches …

Multi-view data processing is an effective tool to differentiate the levels of consumers on
electronics. Recently, the graph based multi-view clustering methods have attracted …

Abstract Machine learning has been used in the past for the auxiliary diagnosis of
Alzheimer's Disease (AD). However, most existing technologies only explore single-view …

Multi-view clustering is widely used to improve clustering performance. Recently, the
subspace clustering tensor learning method based on Markov chain is a crucial branch of …