Results 61 to 70 of about 19,436 (200)
, 2018 In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity.
Cao, Jianting +4 more
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International Journal of Finance &Economics, EarlyView.ABSTRACT This study examines the environmental, social and governance (ESG) scoring methodologies used by Bloomberg and S&P Global through the lens of Data Envelopment Analysis (DEA). It addresses a notable gap in the literature by identifying the underlying factors that shape ESG scores and providing practical insights for companies seeking to ...
Philipe Balan +4 more
wiley +1 more source
Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
IEEE Access, 2019 Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar ...
Min Liu, Xinggan Zhang, Lan Tang
doaj +1 more source
N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
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Cardiac MR Fingerprinting at 0.55T Using a Deep Image Prior for Joint T1, T2, and M0 Mapping
Journal of Magnetic Resonance Imaging, EarlyView.ABSTRACT Background 0.55T systems offer unique advantages and may support expanded access to cardiac MRI. Purpose To assess the feasibility of 0.55T cardiac MR Fingerprinting (MRF), leveraging a deep image prior reconstruction to mitigate noise. Study Type Phantom and prospective in vivo assessment.
Zhongnan Liu +9 more
wiley +1 more source
IEEE AccessElectroencephalography (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 +3 more
doaj +1 more source
Efficient singular-value decomposition of the coupled-cluster triple excitation amplitudes
, 2019 We demonstrate a novel technique to obtain singular-value decomposition (SVD) of the coupled-cluster triple excitations amplitudes, $t_{ijk}^{abc}$. The presented method is based on the Golub-Kahan bidiagonalisation strategy and does not require $t_{ijk}^
Lesiuk, Michal
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Modelling Motion‐Induced Signal Corruption in Steady‐State Diffusion MRI
Magnetic Resonance in Medicine, EarlyView.ABSTRACT Purpose Diffusion‐weighted steady‐state free precession (DW‐SSFP) is a diffusion imaging sequence achieving high SNR efficiency. A key challenge for in vivo DW‐SSFP is the sequence's severe motion sensitivity, currently limiting investigations to low or no motion regimes.
Benjamin C. Tendler +3 more
wiley +1 more source
Robust Tensor Factorization for Color Image and Grayscale Video Recovery
IEEE Access, 2020 Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory.
Shiqiang Du +4 more
doaj +1 more source
Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems [PDF]
, 2014 In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e.,
Cichocki, Andrzej
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