Optimal rank selection is an important issue in tensor decomposition problems, especially
for Tensor Train (TT) and Tensor Ring (TR)(also known as Tensor Chain) decompositions. In …

Randomized algorithms are efficient techniques for big data tensor analysis. In this tutorial
paper, we review and extend a variety of randomized algorithms for decomposing large …

While tensor ring (TR) decomposition methods have been extensively studied, the
determination of TR-ranks remains a challenging problem, with existing methods being …

In this paper, a coarray tensor completion-based direction-of-arrival (DOA) estimation
method is proposed for coprime planar array. To perform Nyquist-matched coarray signal …

A rising problem in the compression of Deep Neural Networks is how to reduce the number
of parameters in convolutional kernels and the complexity of these layers by low-rank tensor …

Tucker decomposition is the generalization of Principal Component Analysis to high-order
tensors. The L2-norm-based formulation of standard Tucker suffers from severe sensitivity to …

Tucker decomposition is a standard method for the analysis of high-order tensor data.
Standard Tucker decomposition generalizes singular-value decomposition and is …

The vast volume of heterogeneous multi-source data generated by terminal devices is
expanding exponentially as the Intelligent Internet of Things develops. The construction of …