Results 31 to 40 of about 1,788,984 (247)
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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, 2022 In this paper we establish necessary and sufficient conditions for the existence of line segments (or flats) in the sphere of the nuclear norm via the notion of simultaneous polarization and a refined expression for the subdifferential of the nuclear norm.
Hoheisel, Tim, Paquette, Elliot
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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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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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A concise proof to the spectral and nuclear norm bounds through tensor partitions
Open Mathematics, 2019 On estimations of the lower and upper bounds for the spectral and nuclear norm of a tensor, Li established neat bounds for the two norms based on regular tensor partitions, and proposed a conjecture for the same bounds to be hold based on general tensor ...
Kong Xu
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Weighted Tensor Nuclear Norm Minimization for Color Image Restoration
IEEE Access, 2019 Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance.
Kaito Hosono +2 more
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IEEE Access, 2018 Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment.
Naveed Iqbal +5 more
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BMC Bioinformatics, 2022 Background Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated ...
Qi Liu
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Image Classification Using Low-Rank Regularized Extreme Learning Machine
IEEE Access, 2019 Extreme learning machine (ELM), a least-square-based learning algorithm, is a competitive machine learning method and provides efficient unified learning solutions for the applications of classification and regression.
Qin Li +4 more
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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 ...
Ming Yuan 0001, Cun-Hui Zhang
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