Results 241 to 250 of about 240,751 (277)

Dynamic MRI Reconstruction Combining Tensor Nuclear Norm and Casorati Matrix Nuclear Norm

ISMRM Annual Meeting, 2023
Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion. Inspired by the different properties of the tensor nuclear norm (TNN) and the Casorati matrix nuclear norm (MNN), we introduce a novel dMRI ...
Yinghao Zhang, Yue Hu, Xin Lu
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Nuclear norm-regularized SENSE reconstruction

Magnetic Resonance Imaging, 2012
SENSitivity Encoding (SENSE) is a mathematically optimal parallel magnetic resonance (MRI) imaging technique when the coil sensitivities are known. In recent times, compressed sensing (CS)-based techniques are incorporated within the SENSE reconstruction framework to recover the underlying MR image.
Angshul, Majumdar, Rabab K, Ward
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Nuclear Norm Based 2DPCA

2013 2nd IAPR Asian Conference on Pattern Recognition, 2013
This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is
Fanlong Zhang, Jianjun Qian, Jian Yang
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Nuclear Norms and German Nuclear Interests

Comparative Strategy, 2001
A broad norm against nuclear weapons has not established itself in Germany. A brief overview of Germany's current "non-debate" regarding nuclear weapons shows that discussion concentrates on related issues like proliferation, missile defense, and arms control, and not on NATO strategy per se.
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Nuclear Norm Regularized Sparse Coding

2014 22nd International Conference on Pattern Recognition, 2014
Partially occluded or illuminated faces pose a significant obstacle for robust, real-world face recognition. The problem of how to characterize the error caused by occlusion or illumination is still a challenging task. There must exist some close relationship between the error metric and error distribution. However, some metric (e.g.
Lei Luo, Jian Yang, Jianjun Qian, Jingyu Yang   +3 more
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Nuclear Norm Regularization

WIREs Computational Statistics
ABSTRACTNuclear norm, also known as trace norm, has been widely used in statistical machine learning. Nuclear norm regularization has emerged as an important tool for addressing various statistical problems involving the estimation of low‐rank matrices, particularly in tasks such as matrix completion and reduced rank regression. This review delves into
Dengdeng Yu, Dehan Kong
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Nuclear norm regularization for overparametrized Hammerstein systems

49th IEEE Conference on Decision and Control (CDC), 2010
In this paper we study the overparametrization scheme for Hammerstein systems [1] in the presence of regularization. The quality of the convex approximation is analysed, that is obtained by relaxing the implicit rank one constraint. To obtain an improved convex relaxation we propose the use of nuclear norms [2], instead of using ridge regression.
Falck, Tillmann, Suykens, Johan, Schoukens, Joannes, De Moor, Bart   +3 more
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Principal Component Analysis based on Nuclear norm Minimization

Neural Networks, 2019
Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent years, massive efforts have been made to improve the robustness of PCA.
Mi, Jian-Xun, Zhang, Ya-Nan, Lai, Zhihui, Li, Weisheng, Zhou, Lifang, Zhong, Fujin   +5 more
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Disarmament and Other Nuclear Norms

The Washington Quarterly, 2013
Once the Cold War ended, and with it the prospect of a cataclysmic Third World War, many argued that the nuclear arsenals accumulated during its 45 years were anachronistic and redundant.
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Nuclear Norm Based Bidirectional 2DPCA

2014
This paper develops a new image feature extraction and recognition method coined bidirectional compressed nuclear-norm based 2DPCA (BN2DPCA). BN2DPCA presents a sequentially optimal image compression mechanism, making the information of the image compact into its up-left corner. BN2DPCA is tested using the Extended Yale B and the CMU PIE face databases.
Yu Ding, Caikou Chen, Ya Gu, Yu Wang
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