Results 51 to 60 of about 1,664 (280)
Low-Rank Tensor Completion via Tensor Nuclear Norm With Hybrid Smooth Regularization
IEEE Access, 2019 As a convex surrogate of tensor multi rank, recently the tensor nuclear norm (TNN) obtains promising results in the tensor completion. However, only considering the low-tubal-rank prior is not enough for recovering the target tensor, especially when the ...
Xi-Le Zhao +4 more
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Convex Recovery of Tensors Using Nuclear Norm Penalization [PDF]
, 2015 The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex analysis and optimization over spaces of tensors is now gaining much interest due to its potential applications to signal processing, statistics and engineering. The goal of this
Stéphane Chrétien, Tianwen Wei
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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
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Spatiotemporal traffic data imputation via tensorial weighted Schatten‐p norm minimization
IET Intelligent Transport Systems, 2022 Spatiotemporal traffic data exhibit multi‐granular low‐rank structure due to their periodicity among different timelines. Traditional low rank data completion methods fail to characterize such properties and produce unsatisfactory results for data ...
Shaofan Wang +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The hyperspectral image (HSI) is easily contaminated by various kinds of mixed noise (such as Gaussian noise, impulse noise, stripes, and deadlines) during the process of data acquisition and conversion, which significantly affect the quality and ...
Pengfei Liu, Lanlan Liu, Liang Xiao
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Nuclear Norm Under Tensor Kronecker Products
CoRR, 2020 Derksen proved that the spectral norm is multiplicative with respect to vertical tensor products (also known as tensor Kronecker products). We will use this result to show that the nuclear norm and other norms of interest are also multiplicative with respect to vertical tensor products.
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Advancing Age Modulates Associations Between Cognitive Impairment and Brain Volumes in Early MS
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Introduction Cognitive impairment is common in multiple sclerosis (MS), but manifestations following the first demyelinating event are relatively unexplored. We investigated cross‐sectional associations between magnetic resonance imaging (MRI)–derived brain volumes and the presence of cognitive impairment outcomes five years after the first ...
Piriyankan Ananthavarathan +14 more
wiley +1 more source
Low-rank tensor completion with a new tensor nuclear norm induced by invertible linear transforms
, 2019 © 2019 IEEE. This work studies the low-rank tensor completion problem, which aims to exactly recover a low-rank tensor from partially observed entries.
Peng X +5 more
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Advanced Engineering Materials, EarlyView.This study demonstrates an efficient recycling route for out‐of‐spec AlSi10Mg atomized powders through compaction and arc remelting followed by suction casting. By correlating compaction load, cooling rate, and resulting microstructure, we show that intermediate pressures (50–80 kN) and rapid cooling refine dendrites, reduce porosity, and enhance ...
Mila Christy de Oliveira +4 more
wiley +1 more source
Multiple Spectral-Spatial Representation Based on Tensor Decomposition for HSI Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 To exploit the spectral-spatial information of hyperspectral image (HSI) and achieve higher the detection accuracy, a novel multiple spectral-spatial representation based on tensor decomposition method is proposed for HSI anomaly detection (AD) in this ...
Yujian Wang +5 more
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