With the widespread deployment of sensors and the Internet-of-Things, multi-view data has
become more common and publicly available. Compared to traditional data that describes …

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 …

The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method,
which clusters data points with latent representation and simultaneously explores underlying …

Graph and subspace clustering methods have become the mainstream of multi-view
clustering due to their promising performance. However,(1) since graph clustering methods …

Previous multi-view clustering algorithms mostly partition the multi-view data in their original
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …

This paper studies the subspace clustering problem. Given some data points approximately
drawn from a union of subspaces, the goal is to group these data points into their underlying …

Abstract Multi-View Clustering (MVC) has garnered more attention recently since many real-
world data are comprised of different representations or views. The key is to explore …

Existing subspace clustering methods typically employ shallow models to estimate
underlying subspaces of unlabeled data points and cluster them into corresponding groups …

In this paper, we focus on how to boost the multi-view clustering by exploring the
complementary information among multi-view features. A multi-view clustering framework …