Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

Multi-view clustering (MVC) has attracted more and more attention in the recent few years by
making full use of complementary and consensus information between multiple views to …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

We present convolutional neural network based approaches for unsupervised multimodal
subspace clustering. The proposed framework consists of three main stages—multimodal …

Self-representation based subspace learning has shown its effectiveness in many
applications. In this paper, we promote the traditional subspace representation learning by …

In recent years, combining multiple sources or views of datasets for data clustering has been
a popular practice for improving clustering accuracy. As different views are different …

Multi-view subspace clustering is essential to many scientific problems. However, most
existing methods suffer from three aspects of issues. First, these methods usually adopt a …

For graph-based multi-view clustering, a critical issue is to capture consensus cluster
structures via a two-stage learning scheme. Specifically, first learn similarity graph matrices …

Recent years, more and more multi-view data are widely used in many real-world
applications. This kind of data (such as image data) is high dimensional and obtained from …

In real world applications, more and more data, for example, image/video data, are high
dimensional and represented by multiple views which describe different perspectives of the …