Recommender systems that predict the preference of users have attracted more and more
attention in decades. One of the most popular methods in this field is collaborative filtering …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …

Low-rank matrix approximation (LRMA)-based methods have made a great success for
grayscale image processing. When handling color images, LRMA either restores each color …

With the development of high-throughput technology and the accumulation of biomedical
data, the prior information of biological entity can be calculated from different aspects …

Multiview subspace clustering (MVSC) has been used to explore the internal structure of
multiview datasets by revealing unique information from different views. Most existing …

This paper develops a new class of nonconvex regularizers for low-rank matrix recovery.
Many regularizers are motivated as convex relaxations of the\emph {matrix rank} function …

With the growing interest in convex and nonconvex low-rank matrix learning problems, the
widely used singular value thresholding (SVT) operators associated with rank relaxation …

Low-rank self-representation based subspace learning has confirmed its great effectiveness
in a broad range of applications. Nevertheless, existing studies mainly focus on exploring …

Traffic anomaly detection is critical for advanced Internet management. Existing detection
algorithms generally convert the high-dimensional data to a long vector, which compromises …