In compressed sensing, a small number of linear measurements can be used to reconstruct
an unknown signal. Existing approaches leverage assumptions on the structure of these …

Tensor principal component pursuit (TPCP) is a powerful approach in the tensor robust
principal component analysis (TRPCA), where the goal is to decompose a data tensor to a …

Tucker tensor decomposition (TD) is widely used for image representation, reconstruction,
and learning tasks. Compared to principal component analysis (PCA) models, tensor …

In this article, a curious phenomenon in the tensor recovery algorithm is considered: can the
same recovered results be obtained when the observation tensors in the algorithm are …

Abstract Language is dynamic, constantly evolving and adapting with respect to time,
domain or topic. The adaptability of language is an active research area, where researchers …

In this paper, we propose a new sparsity regularizer for measuring the low-rank structure
underneath a tensor. The proposed sparsity measure has a natural physical meaning which …

Aligning a group of linearly correlated images is an important task in computer vision. In this
paper, we propose a combination of transformed tensor nuclear norm and tensor ℓ _1 ℓ 1 …

In this paper, we propose a definition of Generalized Tensor Total Variation norm (GTV) that
considers both the inhomogeneity and the multi-directionality of responses to derivative-like …

Recently, the $\ell^ 4$-norm maximization has been proposed to solve the sparse dictionary
learning (SDL) problem. The simple MSP (matching, stretching, and projection) algorithm …

While recent techniques for discriminative dictionary learning have attained promising
results on the classification tasks, their performance is highly dependent on the number of …