Graph-based sparse feature selection plays an important role in semi-supervised feature
selection, which greatly improves the performance of feature selection. However, most …

Multi-view clustering has attracted extensive attention since it can integrate the
complementary information of different views. Nonetheless, most existing methods suffer …

The subspace clustering algorithms for image datasets apply a self-expression coefficient
matrix to obtain the correlation between samples and then perform clustering. However …

Despite the good clustering performance, most of existing multi-view subspace clustering
methods fail to consider the non-linear relationships of high-dimensional data, higher-order …

Recently, the popularity of multi-view clustering (MVC) has increased, and many MVC
methods have been developed. However, the affinity matrix that is learned by the MVC …

Complex systems in society and nature cannot be effectively modeled and represented by a
single perspective, resulting in the so-called multi-view data, which provide an absolutely …

Recently, multi-view subspace clustering has attracted intensive attentions due to the
remarkable clustering performance by extracting abundant complementary information from …

Kernel k-means based and spectral clustering (SC) based multi-kernel clustering (MKC) has
been widely used in recent years due to the efficiency in grouping nonlinear data …

Incomplete multi-view clustering, which aims to divide different groups into incomplete views
produced by various sensors, has attracted research attention. In this article, we propose a …

Since multi-view subspace clustering combines the advantages of deep learning to capture
the nonlinear nature of data, deep multi-view subspace clustering methods have …