High-dimensional image representation is a challenging task since data has the intrinsic low-
dimensional and shift-invariant characteristics. Currently, popular methods, such as tensor …

In this paper, we develop the weighted error entropy-regularized tensor learning method for
multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal …

Samples are commonly represented as sparse vectors in many dictionary representation
algorithms. However, this method may result in loss of discriminatory information. Moreover …

A novel online joint kernel learning and clustering (OKC) framework is derived which is
capable of determining time-varying clustering configurations without the need for training …

Ensemble clustering based on co-association matrices integrates multiple connective
matrices from base clusterings to achieve superior results. However, these methods …

Clustering based on local density peaks and graph cut (LDP-SC) is one of the state-of-the-
art algorithms in unsupervised clustering, which first divides the data set to be multiple local …

Semi-supervised clustering provides accurate assignments by leveraging a limited amount
of supervisory information. There are two main groups of conventional semi-supervised …

Multi-view clustering aims to study the complementary information across views and
discover the underlying structure. For solving the relatively high computational cost for the …

Feature extraction plays an indispensable role in image and video technology. However, it is
difficult for traditional matrix based feature extraction methods to handle massive multi …

As one of the most effective subspace clustering methods, the self-expression based
sparsity method leverages the robust representational learning and non-linear …