Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …

Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR
Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It …

Schizophrenia is a mental disorder that causes adverse effects on the mental capacity of a
person, emotional inclinations, and quality of personal and social life. The official statistics …

Kronecker regression is a highly-structured least squares problem $\min_ {\mathbf
{x}}\lVert\mathbf {K}\mathbf {x}-\mathbf {b}\rVert_ {2}^ 2$, where the design matrix $\mathbf …

Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The
widely used alternating least squares (ALS) method, which solves a sequence of over …

The low-rank canonical polyadic tensor decomposition is useful in data analysis and can be
computed by solving a sequence of overdetermined least squares subproblems. Motivated …

The tensor-train (TT) format is a highly compact low-rank representation for high-
dimensional tensors. TT is particularly useful when representing approximations to the …

This paper presents a framework that suggests the utilization of cross tensor approximation
or tensor CUR approximation to reconstruct incomplete images and videos. The proposed …

We introduce a Tucker tensor cross approximation method that constructs a low-rank
representation of a d-dimensional tensor by sparsely sampling its fibers. These fibers are …

Low rank tensor approximation is a fundamental tool in modern machine learning and data
science. In this paper, we study the characterization, perturbation analysis, and an efficient …