A contemporary and comprehensive survey on streaming tensor decomposition
K Abed-Meraim, NL Trung… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …
applications, from neuroscience and wireless communications to social networks. In an …
Tensor methods in computer vision and deep learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD)
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 …
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …
Uncovering human behavioral heterogeneity in urban mobility under the impacts of disruptive weather events
Understanding the response of human mobility to disruptive weather events is beneficial for
the development of urban risk mitigation and emergency response policies, thus enhancing …
the development of urban risk mitigation and emergency response policies, thus enhancing …
Differentiate tensor low rank soft decomposition in thermography defect detection
Composites are prone to defects in manufacture, which are to be evaluated for safety
through non-destructive testing (NDT) techniques. Thermal images are acquired for NDT by …
through non-destructive testing (NDT) techniques. Thermal images are acquired for NDT by …
Dynamic L1-norm Tucker tensor decomposition
DG Chachlakis, M Dhanaraj… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Tucker decomposition is a standard method for processing multi-way (tensor)
measurements and finds many applications in machine learning and data mining, among …
measurements and finds many applications in machine learning and data mining, among …
Spatial–temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
Q Li, X Yang, Y Wang, Y Wu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers take
control measures and drivers to choose the optimal travel routes. Recently, graph …
control measures and drivers to choose the optimal travel routes. Recently, graph …
The automatic detection of seizure based on tensor distance and bayesian linear discriminant analysis
Electroencephalogram (EEG) plays an important role in recording brain activity to diagnose
epilepsy. However, it is not only laborious, but also not very cost effective for medical experts …
epilepsy. However, it is not only laborious, but also not very cost effective for medical experts …
Near-linear time and fixed-parameter tractable algorithms for tensor decompositions
AV Mahankali, DP Woodruff, Z Zhang - arXiv preprint arXiv:2207.07417, 2022 - arxiv.org
We study low rank approximation of tensors, focusing on the tensor train and Tucker
decompositions, as well as approximations with tree tensor networks and more general …
decompositions, as well as approximations with tree tensor networks and more general …
Robust Low-Rank Tensor Decomposition with the L2 Criterion
The growing prevalence of tensor data, or multiway arrays, in science and engineering
applications motivates the need for tensor decompositions that are robust against outliers. In …
applications motivates the need for tensor decompositions that are robust against outliers. In …