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 …

In many applications, high-dimensional data points can be well represented by low-
dimensional subspaces. To identify the subspaces, it is important to capture a global and …

Without any prior structure information, nuclear norm minimization (NNM), a convex
relaxation for rank minimization (RM), is a widespread tool for matrix completion and …

Face sketch synthesis is useful and profitable in digital entertainment. Most existing face
sketch synthesis methods rely on the assumption that facial photographs/sketches form a …

In this paper we consider low-rank estimation of room impulse responses (RIRs). Inspired by
a physics-driven room-acoustical model, we propose an estimator of RIRs that promotes a …

The problem of low-rank approximation with convex constraints, which appears in data
analysis, system identification, model order reduction, low-order controller design, and low …

Low-rank plus sparse matrix decomposition (LSD) is an important problem in computer
vision and machine learning. It has been solved using convex relaxations of the matrix rank …

We provide theory for the computation of convex envelopes of non-convex functionals
including an ℓ^ 2 ℓ 2-term and use these to suggest a method for regularizing a more …

As well known, low rank representation method (LRR) has obtained promising performance
for subspace clustering, and many LRR variants have been developed, which mainly solve …

This paper proposes a new model to remove the salt and pepper (SAP) noise problem. In
the proposed method, we combine the high order total variation regularization with the …