Tensor decompositions for hyperspectral data processing in remote sensing: A comprehensive review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …
(RS) imaging has provided a significant amount of spatial and spectral information for the …
Data-driven evolutionary optimization: An overview and case studies
Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …
constraint functions is straightforward. In solving many real-world optimization problems …
Robust online tensor completion for IoT streaming data recovery
C Liu, T Wu, Z Li, T Ma, J Huang - IEEE transactions on neural …, 2022 - ieeexplore.ieee.org
Reliable data measurement is considered to be one of the critical ingredients for variant
Internet of Things (IoT) applications. Gaining full knowledge of measurement data is …
Internet of Things (IoT) applications. Gaining full knowledge of measurement data is …
Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion
Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
Tensor completion algorithms in big data analytics
Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …
observed tensors. Due to the multidimensional character of tensors in describing complex …
Data-driven evolutionary algorithm with perturbation-based ensemble surrogates
Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive
optimization, which is useful and efficient when the objective function of the optimization …
optimization, which is useful and efficient when the objective function of the optimization …
Boosting data-driven evolutionary algorithm with localized data generation
By efficiently building and exploiting surrogates, data-driven evolutionary algorithms
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …
Tensor ring decomposition with rank minimization on latent space: An efficient approach for tensor completion
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from
the laborious model selection problem due to their high model sensitivity. In particular, for …
the laborious model selection problem due to their high model sensitivity. In particular, for …
An efficient recursive identification algorithm for multilinear systems based on tensor decomposition
Y Wang, L Yang - … Journal of Robust and Nonlinear Control, 2021 - Wiley Online Library
There are many important fields involving the multilinear system identification. A great
number of parameters to be identified is an important challenge, leading to the need for …
number of parameters to be identified is an important challenge, leading to the need for …