Results 21 to 30 of about 240,751 (277)
Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization
IEEE Access, 2019 In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor ranks.
Yaru Su, Xiaohui Wu, Wenxi Liu
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IEEE Access, 2018 Magnetic resonance spectroscopy (MRS) has many important applications in medical imaging, biology, and chemistry. The 1-D MRS is too crowded for complex samples to retrieve chemical or biological information.
Di Guo, Xiaobo Qu
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral anomaly detection is an important technique in the field of remote sensing image processing. Over the last few years, low rank and sparse matrix decomposition (LRSMD) has played an increasingly significant role in hyperspectral anomaly ...
Xiaoyi Wang +4 more
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Large factor model estimation by nuclear norm plus $l_1$ norm penalization
, 2021 This paper provides a comprehensive estimation framework via nuclear norm plus $l_1$ norm penalization for high-dimensional approximate factor models with a sparse residual covariance. The underlying assumptions allow for non-pervasive latent eigenvalues and a prominent residual covariance pattern.
Farne, Matteo, Montanari, Angela
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Joint Local Abundance Sparse Unmixing for Hyperspectral Images
Remote Sensing, 2017 Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity.
Mia Rizkinia, Masahiro Okuda
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Recursive Nuclear Norm based Subspace Identification
IFAC-PapersOnLine, 2017 Abstract Nuclear norm based subspace identification methods have recently gained importance due to their ability to find low rank solutions while maintaining accuracy through convex optimization. However, their heavy computational burden typically precludes the use in an online, recursive manner, such as may be required for adaptive control.
Telsang, B. (author) +2 more
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A Unified Scalable Equivalent Formulation for Schatten Quasi-Norms
Mathematics, 2020 The Schatten quasi-norm is an approximation of the rank, which is tighter than the nuclear norm. However, most Schatten quasi-norm minimization (SQNM) algorithms suffer from high computational cost to compute the singular value decomposition (SVD) of ...
Fanhua Shang +5 more
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Online Matrix Completion Through Nuclear Norm Regularisation [PDF]
, 2006 It is the main goal of this paper to propose a novel method to perform matrix completion on-line. Motivated by a wide variety of applications, ranging from the design of recommender systems to sensor network localization through seismic data ...
Hui, Xiaoyun +5 more
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Nuclear norm of higher-order tensors
Mathematics of Computation, 2017 23 ...
Friedland, Shmuel, Lim, Lek-Heng
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On Tensor Completion via Nuclear Norm Minimization [PDF]
Foundations of Computational Mathematics, 2015 Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply ...
Yuan, Ming, Zhang, Cun-Hui
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