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 …

This paper argues that randomized linear sketching is a natural tool for on-the-fly
compression of data matrices that arise from large-scale scientific simulations and data …

Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …

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 …

Many applications in data science and scientific computing involve large-scale datasets that
are expensive to store and manipulate. However, these datasets possess inherent …

We develop a weak-form sparse identification method for interacting particle systems (IPS)
with the primary goals of reducing computational complexity for large particle number N and …

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 …

In this paper, we develop a novel procedure for low-rank tensor regression, namely
Importance Sketching Low-rank Estimation for Tensors (ISLET). The central idea behind …

This paper is devoted to the definition and computation of the tensor complete orthgonal
decomposition of a third-order tensor called t-URV decompositions. We first give the …