Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-
means is one of the well-known algorithms for clustering data. The foundation of this …

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

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 …

Clustering is the most fundamental technique for data processing. This paper presents a
collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ …

Recently, label distribution learning (LDL) has drawn much attention in machine learning,
where LDL model is learned from labelel instances. Different from single-label and multi …

In several fields, deep learning has achieved tremendous success. Multi-view learning is a
workable method for handling data from several sources. For clustering multi-view data …

In recent years, significant progress has been made in Multi-view Subspace Clustering
(MSC). Most existing MSC methods attempt to explore and exploit the view correlations of …

With the powerful ability to exploit the latent structure of self-representation information,
multiple off-the-shelf low rank tensor constraints have been employed in multiview tensor …

The existing multi-view subspace clustering (MVSC) algorithm still has certain limitations.
First, the affinity matrix obtained by them is not clean and robust enough since the original …

This paper explores the problem of multi-view clustering, which aims to promote clustering
performance with multi-view data. The majority of existing methods have problems with …