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

Conventional tensor direction-of-arrival (DOA) estimation methods for sparse arrays apply
canonical polyadic decomposition (CPD) to the high-order coarray covariance tensor for …

We present a new algorithm for incrementally updating the tensor train decomposition of a
stream of tensor data. This new algorithm, called the tensor train incremental core expansion …

Robust tensor tracking or robust adaptive tensor decomposition of streaming tensors is
crucial when observations are corrupted by sparse outliers and missing data. In this paper …

We propose a framework for discrete scientific data compression based on the tensor-train
(TT) decomposition. Our approach is tailored to handle unstructured output data from …

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

In this paper, we propose two new provable algorithms for tracking online low-rank
approximations of high-order streaming tensors with missing data. The first algorithm …

The so-called block-term decomposition (BTD) tensor model, especially in its rank-(L r, L r,
1) version, has been recently receiving increasing attention due to its enhanced ability of …

Streaming tensor factorization is an effective tool for unsupervised analysis of time-evolving
sparse data, which emerge in many critical domains such as cybersecurity and trend …

Tensor tracking which is referred to as online (adaptive) decomposition of streaming tensors
has recently gained much attention in the signal processing community due to the fact that …