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 …

Segmentation of white blood cells in digital haematology microscope images represents one
of the major tools in the diagnosis and evaluation of blood disorders. Pathological …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

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 …

In this paper, we propose a novel classification scheme for the remotely sensed
hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique …

Leveraging on the underlying low-dimensional structure of data, low-rank and sparse
modeling approaches have achieved great success in a wide range of applications …

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the
tensor in a frequency domain, has been found useful in solving low-rank tensor recovery …

Various image datasets appear naturally in the form of multi-dimensional arrays
(hypermatrices), called tensors. Image with incomplete entries, which often can be …

We study the tensor robust principal component analysis (TRPCA) problem, a tensorial
extension of matrix robust principal component analysis, which aims to split the given tensor …

Multi-view clustering integrates multiple feature sets, which usually have a complementary
relationship and can reveal distinct insights of data from different angles, to improve …