Most existing graph-based clustering methods need a predefined graph and their clustering
performance highly depends on the quality of the graph. Aiming to improve the multiview …

Incomplete multi-view clustering, which aims to solve the clustering problem on the
incomplete multi-view data with partial view missing, has received more and more attention …

In this paper, we propose a novel method, referred to as incomplete multi-view tensor
spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging …

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 …

Spectral clustering (SC) has been widely applied to various computer vision tasks, where
the key is to construct a robust affinity matrix for data partitioning. With the increase in visual …

We present a new clustering method in the form of a single clustering equation that is able to
directly discover groupings in the data. The main proposition is that the first neighbor of each …

This paper addresses the problem of video object segmentation, where the initial object
mask is given in the first frame of an input video. We propose a novel spatio-temporal …

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 …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

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