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 …

Solving an ill-posed linear inverse problem requires knowledge about the underlying signal
model. In many applications, this model is a priori unknown and has to be learned from data …

Accurate polyp segmentation is of great significance for the diagnosis and treatment of colon
cancer. Deep convolution network can extract the common high level features of the target …

In this paper, we provide an overview of state-of-the-art techniques that are being developed
for efficient calculation of second and higher nuclear derivatives of quantum mechanical …

We introduce robust principal component analysis from a data matrix in which the entries of
its columns have been corrupted by permutations, termed Unlabeled Principal Component …

Recent advances in matrix completion enable data imputation in full-rank matrices by
exploiting low dimensional (nonlinear) latent structure. In this paper, we develop a new …

This paper develops new methods to recover the missing entries of a high-rank or even full-
rank matrix when the intrinsic dimension of the data is low compared to the ambient …

Conventional matrix completion methods are generally linear because they assume that the
given data are from linear transformations of lower-dimensional latent subspace and the …

We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic
MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low …

In this paper, we introduce a novel kernel low-rank algorithm to recover free-breathing and
ungated dynamic MRI data from highly undersampled measurements. The image frames in …