Low-rank tensor completion aims to recover the missing entries of multi-way data, which has
become popular and vital in many fields such as signal processing and computer vision. It …

With the vigorous development of renewable energy, the installed capacity of photovoltaic
(PV) power plants continuously expands. For distributed PV systems, spatio-temporal short …

Deep neural networks (DNNs) have achieved great success in many data processing
applications. However, high computational complexity and storage cost make deep learning …

Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance
improvement. The advent of multimodal data allows tasks to be referenced by multiple …

Robust tensor principal component analysis (RTPCA) based on tensor singular value
decomposition (t-SVD) separates the low-rank component and the sparse component from …

Modern data analytics applications are increasingly characterized by exceedingly large and
multidimensional data sources. This represents a challenge for traditional machine learning …

In this paper, we consider the tensor-on-tensor association detection problem, where the
goal is to detect whether there is an association between the tensor responses to tensor …

Optimization problems with tensor variables are widely used in statistics, machine learning,
pattern recognition, signal processing, computer vision, etc. Among these applications, the …

Tensor decomposition is widely used in feature extraction, data analysis, and other fields. As
a means of tensor decomposition, the robust tensor power method based on tensor sketch …

Analytics of financial data is inherently a Big Data paradigm, as such data are collected over
many assets, asset classes, countries, and time periods. This represents a challenge for …