Multi-view spectral clustering, which exploits the complementary information among graphs
of diverse views to obtain superior clustering results, has attracted intensive attention …

Multi-view clustering can explore common semantics from multiple views and has attracted
increasing attention. However, existing works punish multiple objectives in the same feature …

Multiview clustering algorithms have attracted intensive attention and achieved superior
performance in various fields recently. Despite the great success of multiview clustering …

Multi-view clustering has gained broad attention owing to its capacity to exploit
complementary information across multiple data views. Although existing methods …

Graph-based methods have hitherto been used to pursue the coherent patterns of data due
to its ease of implementation and efficiency. These methods have been increasingly applied …

Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the
complementary information conveyed by multiple views of objects to be clustered. Recently …

Since the present era is entirely computer and Internet of Things (IoT) oriented, enormous
amounts of data are produced quickly from many sources. Machine learning's primary …

The tensor-based multi-view subspace clustering algorithms have received widespread
attention due to the powerful ability to capture high-order correlation across views. Although …

Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global
and local similarity information from data. In this paper, we propose a novel local similarity …

Aiming at solving the problem of clustering in the multi-view datasets which include samples
with information missing in one or more views, incomplete multi-view clustering has received …