Results 61 to 70 of about 3,137 (156)

Exploring Tensor-Based Optimization for Missing EEG Signal Recovery: A Comparative Study of Optimization Methods Across Different Tensor Decomposition Frameworks

IEEE Access
Electroencephalography (EEG) signals are frequently compromised by missing data due to electrode contact issues or subject movement. Tensor decomposition has emerged as a powerful technique for analyzing multidimensional EEG data.
Yue Zhang, Huanmin Ge, Chencheng Huang, Xinhua Su   +3 more
doaj   +1 more source

Optimizing neutron-gamma discrimination in scintillation detectors using Tucker decomposition

Kuwait Journal of Science
Neutron-gamma discrimination is essential for applications in radiation monitoring and nuclear safety. Conventional methods often face challenges related to complex signal processing and experimental constraints.
Imane Ahnouz, Hanan Arahmane, Rajaa Sebihi   +2 more
doaj   +1 more source

Accelerated Low-Rank Tensor Completion via Projected Tensor Block Coordinate Descent

IEEE Access
The low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis.
Geunseop Lee
doaj   +1 more source

Hyperspectral Image Denoising via $L_{0}$ Regularized Low-Rank Tucker Decomposition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
This article studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the $L_{1}$-norm-based regularizers to remove sparse noise and ...
Xin Tian, Kun Xie, Hanling Zhang
doaj   +1 more source

System-Specific Separable Basis Based on Tucker Decomposition: Application to Density Functional Calculations. [PDF]

J Chem Theory Comput, 2022
Woo J, Kim WY, Choi S.
europepmc   +1 more source

Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window. [PDF]

CNS Neurosci Ther, 2021
Shi LJ, Wei BX, Xu L, Lin YC, Wang YP, Zhang JC.   +5 more
europepmc   +1 more source

rTensor: An R Package for Multidimensional Array (Tensor) Unfolding, Multiplication, and Decomposition

Journal of Statistical Software, 2018
rTensor is an R package designed to provide a common set of operations and decompositions for multidimensional arrays (tensors). We provide an S4 class that wraps around the base 'array' class and overloads familiar operations to users of 'array', and we
James Li, Jacob Bien, Martin T. Wells
doaj   +1 more source

Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications. [PDF]

IEEE Trans Antennas Propag, 2022
Giannakopoulos II, Guryev GD, Serrallés JEC, Georgakis IP, Daniel L, White JK, Lattanzi R.   +6 more
europepmc   +1 more source

Bayesian Adaptive Tucker Decompositions for Tensor Factorization

Journal of Computational and Graphical Statistics
Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibility brings the challenge of selecting the appropriate latent multi-rank. To overcome the issue of pre-selecting the latent multi-rank, we introduce a Bayesian adaptive Tucker decomposition model that ...
Stolf, Federica, Canale, Antonio
openaire   +3 more sources

Tucker Decomposition Network: Expressive Power and Comparison

, 2019
Deep neural networks have achieved a great success in solving many machine learning and computer vision problems. The main contribution of this paper is to develop a deep network based on Tucker tensor decomposition, and analyze its expressive power. It is shown that the expressiveness of Tucker network is more powerful than that of shallow network. In
Liu, Ye, Pan, Junjun, Ng, Michael
openaire   +2 more sources