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

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 …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

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 …

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure
information to improve clustering performance. Recently, many anchor-based variants are …

Although previous graph-based multi-view clustering (MVC) algorithms have gained
significant progress, most of them are still faced with three limitations. First, they often suffer …

Multiview learning (MVL), which enhances the learners' performance by coordinating
complementarity and consistency among different views, has attracted much attention. The …

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

Incomplete multi-view clustering aims to exploit the information of multiple incomplete views
to partition data into their clusters. Existing methods only utilize the pair-wise sample …

Graph learning has emerged as a promising technique for multi-view clustering due to its
efficiency of learning a unified graph from multiple views. Previous multi-view graph learning …