Network embedding is to learn low-dimensional vector representations for nodes in a
network. It has shown to be effective in a variety of tasks such as node classification and link …

Algorithms have never been more important. As the recipes of computer programs,
algorithms rule our lives. Although they can be forces for both good and evil, this is not a …

We propose an inertial Douglas–Rachford splitting algorithm for finding the set of zeros of
the sum of two maximally monotone operators in Hilbert spaces and investigate its …

Clustering is a fundamental problem in many scientific applications. Standard methods such
as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by …

The Mumford--Shah model is one of the most important image segmentation models and
has been studied extensively in the last twenty years. In this paper, we propose a two-stage …

In the biclustering problem, we seek to simultaneously group observations and features.
While biclustering has applications in a wide array of domains, ranging from text mining to …

Selective image segmentation is an important topic in medical imaging and real
applications. In this paper, we propose a weighted variational selective image segmentation …

We present a novel clustering method, formulated as a convex optimization problem. The
method is based on over-parameterization and uses a sum-of-norms (SON) regularization to …

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data.
Prevalent clustering methods mainly focus on vector or matrix-variate data and are not …

To improve the image segmentation quality, it is important to adequately describe the local
features of targets in images. In this paper, we develop a novel adaptive total variation …