Riemannian Newton optimization methods for the symmetric tensor approximation problem
Sign Up to like & getrecommendations! Published in 2021 at "Linear Algebra and its Applications"
DOI: 10.1016/j.laa.2021.12.008
Abstract: The Symmetric Tensor Approximation problem (STA) consists of approximating a symmetric tensor or a homogeneous polynomial by a linear combination of symmetric rank-1 tensors or powers of linear forms of low symmetric rank. We present… read more here.
Keywords: newton; tensor; symmetric tensor; approximation problem ... See more keywords
Hyperspectral Image Restoration via Subspace-Based Nonlocal Low-Rank Tensor Approximation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3141801
Abstract: In this letter, we present a subspace-based nonlocal low-rank tensor approximation framework (SNLRTA) for hyperspectral image (HSI) restoration. The proposed method consists of a subspace learning method to achieve an accurate subspace characterization of HSI… read more here.
Keywords: rank tensor; tensor approximation; low rank; subspace ... See more keywords
Hyperspectral Restoration and Fusion With Multispectral Imagery via Low-Rank Tensor-Approximation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2020.3049014
Abstract: Tensor-based fusion that couples the high spatial resolution of a multispectral image (MSI) to the high spectral resolution of a hyperspectral image (HSI) is considered. The fusion problem is first formulated mathematically as a convex… read more here.
Keywords: tensor; fusion; low rank; tensor approximation ... See more keywords
Novel Hybrid Low-Rank Tensor Approximation for Hyperspectral Image Mixed Denoising Based on Global-Guided-Nonlocal Prior Mechanism
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3217051
Abstract: Hyperspectral image (HSI) mixed denoising is a challenging task in the fields of remote sensing, environmental monitoring, mineral exploration, and so on. A crucial difficulty is to acquire clean restoration from HSIs that encounter Gaussian… read more here.
Keywords: rank tensor; nonlocal prior; tensor approximation; low rank ... See more keywords
Data-Driven Low-Rank Tensor Approximation for Fast Grid Integration of Commercial EV Charging Stations Considering Demand Uncertainties
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Smart Grid"
DOI: 10.1109/tsg.2022.3191530
Abstract: The high power demand and charging variation of commercial fast electric vehicle (EV) charging stations has the potential to significantly impact the operation of electric power distribution systems. To evaluate this impact, the engineering practice… read more here.
Keywords: charging stations; rank tensor; tensor approximation; low rank ... See more keywords
A Levenberg-Marquardt Method for Tensor Approximation
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DOI: 10.3390/sym15030694
Abstract: This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for the nonlinear least square problem, to decompose large-scale tensors into the sum of the products of vector groups of a given scale,… read more here.
Keywords: marquardt method; approximation; tensor approximation; levenberg marquardt ... See more keywords