Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

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 …

Multi-view clustering has attracted intensive attention due to the effectiveness of exploiting
multiple views of data. However, most existing multi-view clustering methods only aim to …

With the ability to exploit the internal structure of data, graph-based models have received a
lot of attention and have achieved great success in multiview subspace clustering for …

Most existing multiview clustering methods take graphs, which are usually predefined
independently in each view, as input to uncover data distribution. These methods ignore the …

A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it
usually requires a relatively long time to record sufficient electroencephalogram (EEG) data …

Multiview learning has received extensive research interest and has demonstrated
promising results in recent years. Despite the progress made, there are two significant …

For many real-world multimedia applications, data are often described by multiple views.
Therefore, multi-view learning researches are of great significance. Traditional multi-view …

How to economically cluster large-scale multi-view images is a long-standing problem in
computer vision. To tackle this challenge, this paper introduces a novel approach named …

Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to
identify the associations between the brain imaging phenotypes (ie, regional volumetric …