Multi-view subspace clustering has attracted intensive attention to effectively fuse multi-view
information by exploring appropriate graph structures. Although existing works have made …

Despite the impressive clustering performance and efficiency in characterizing both the
relationship between the data and cluster structure, most existing graph-based multi-view …

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 …

Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the
emergence of multi-view data with missing views in real applications. Recent methods …

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 …

Abstract Recently, Multi-view Representation Learning (MRL) has drawn immense
attentions in the analysis of multi-source data and ubiquitously employed across different …

Among various multi-view clustering approaches, tensor-based multi-view subspace
clustering methods aim to explore the high-order correlations across varying views and have …

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a
consensus representation from different views but ignore the important information hidden in …

In real applications, it is often that the collected multiview data contain missing views. Most
existing incomplete multiview clustering (IMVC) methods cannot fully utilize the underlying …