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 …

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 …

Multi-view subspace clustering aims to partition a set of multi-source data into their
underlying groups. To boost the performance of multi-view clustering, numerous subspace …

For many computer vision applications, the data sets distribute on certain low-dimensional
subspaces. Subspace clustering is to find such underlying subspaces and cluster the data …

In recent years, numerous multi-view subspace clustering methods have been proposed to
exploit the complementary information from multiple views. Most of them perform data …

In this paper, we explore the problem of multiview subspace clustering. We introduce a low-
rank tensor constraint to explore the complementary information from multiple views and …

Subspace clustering is a powerful technology for clustering data according to the underlying
subspaces. Representation based methods are the most popular subspace clustering …

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 …

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 …

In this paper, we propose a structured general and specific multi-view subspace clustering
method for image clustering. Unlike most existing multi-view subspace clustering methods …