Results 41 to 50 of about 240,751 (277)
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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IEEE Access, 2022 This paper provides a subspace method for closed-loop identification, which clearly specifies the model order from noisy measurement data. The method can handle long I/O data of the target system to be noise-tolerant and determine the model order via ...
Ichiro Maruta, Toshiharu Sugie
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Multi-channel nuclear norm minus Frobenius norm minimization for color image denoising
Signal Processing, 2023 Color image denoising is frequently encountered in various image processing and computer vision tasks. One traditional strategy is to convert the RGB image to a less correlated color space and denoise each channel of the new space separately. However, such a strategy can not fully exploit the correlated information between channels and is inadequate to
Yiwen Shan, Dong Hu, Zhi Wang, Tao Jia
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Improving compressed sensing with the diamond norm
, 2016 In low-rank matrix recovery, one aims to reconstruct a low-rank matrix from a minimal number of linear measurements. Within the paradigm of compressed sensing, this is made computationally efficient by minimizing the nuclear norm as a convex surrogate ...
Eisert, Jens +3 more
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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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Hyper-Laplacian Regularized Multi-View Subspace Clustering With a New Weighted Tensor Nuclear Norm
IEEE Access, 2021 In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with a new weighted tensor nuclear norm for multi-view subspace clustering. Specifically, we firstly stack the subspace representation matrices of the different views into a tensor ...
Qingjiang Xiao +4 more
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High‐dimensional regression coefficient estimation by nuclear norm plus l1 norm penalization
Stat, 2023 We propose a new estimator of the regression coefficients for a high‐dimensional linear regression model, which is derived by replacing the sample predictor covariance matrix in the ordinary least square (OLS) estimator with a different predictor covariance matrix estimate obtained by a nuclear norm plus norm penalization.
Farne, Matteo, Montanari, Angela
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Low-Rank Inducing Norms with Optimality Interpretations
, 2018 Optimization problems with rank constraints appear in many diverse fields such as control, machine learning and image analysis. Since the rank constraint is non-convex, these problems are often approximately solved via convex relaxations.
Giselsson, Pontus, Grussler, Christian
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Subsampled Blind Deconvolution via Nuclear Norm Minimization [PDF]
, 2020 Many phenomena can be modeled as systems that preform convolution, including negative effects on data like translation/motion blurs. Blind Deconvolution (BD) is a process used to reverse the negative effects of a system by effectively undoing the ...
Thieken, Alexander
core
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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