Unsupervised feature selection attempts to select a small number of discriminative features
from original high-dimensional data and preserve the intrinsic data structure without using …

Multi-view clustering divides similar objects into the same class through using the fused
multiview information. Most multi-view clustering methods obtain clustering result by only …

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

View missing and label missing are two challenging problems in the applications of multi-
view multi-label classification scenery. In the past years, many efforts have been made to …

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

Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by
categorizing data samples into clusters. Deep learning-based methods exhibit strong feature …

Abstract Multi-View Clustering (MVC) is an emerging research area aiming to cluster
multiple views of the same data, which has recently drawn substantial attention. Various …

Multiview subspace clustering (MVSC) has been used to explore the internal structure of
multiview datasets by revealing unique information from different views. Most existing …

In the area of clustering, multi-view clustering has drawn a lot of research attention by
making full use of information from different views. In many practical applications, collecting …

Unsupervised bipartite graph learning has been a hot topic in multi-view clustering, to tackle
the restricted scalability issue of traditional full graph clustering in large-scale applications …