A graph is usually formed to reveal the relationship between data points and graph structure
is encoded by the affinity matrix. Most graph-based multiview clustering methods use …

A panoply of multi-view clustering algorithms has been developed to deal with prevalent
multi-view data. Among them, spectral clustering-based methods have drawn much attention …

Multi-view clustering (MVC) has been extensively studied to collect multiple source
information in recent years. One typical type of MVC methods is based on matrix …

Abstract Multi-view clustering (MVC), which aims to explore the underlying structure of data
by leveraging heterogeneous information of different views, has brought along a growth of …

Matrix factorization is an important technology that obtains the latent representation of data
by mining the potential structure of data. As two popular matrix factorization techniques …

Multi-view clustering (MVC) holds a significant role in domains like machine learning, data
mining, and pattern recognition. Despite the development of numerous new MVC …

Spectral clustering has been a hot topic in unsupervised learning owing to its remarkable
clustering effectiveness and well-defined framework. Despite this, due to its high …

Multi-view subspace clustering, which partitions multi-view data into their respective
underlying subspaces, has achieved the remarkable clustering performance by extracting …

The quality of obtained features by representation learning determines the performance of a
learning algorithm and subsequent application tasks (eg, high-dimensional data clustering) …

Multiview clustering has aroused increasing attention in recent years since real-world data
are always comprised of multiple features or views. Despite the existing clustering methods …