Results 231 to 240 of about 1,101,519 (253)
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arXiv.org, 2020
In this paper we derive optimal rank-1 approximations with Hankel or Toeplitz structure with regard to two different matrix norms, the Frobenius norm and the spectral norm.
Hanna Knirsch, M. Petz, G. Plonka-Hoch
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In this paper we derive optimal rank-1 approximations with Hankel or Toeplitz structure with regard to two different matrix norms, the Frobenius norm and the spectral norm.
Hanna Knirsch, M. Petz, G. Plonka-Hoch
semanticscholar +1 more source
Joint Frobenius norm and reweighted nuclear norm minimization for interference alignment
2013 IEEE International Conference on Communications (ICC), 2013This paper considers a K-user multiple-input multiple-output (MIMO) interference channel in which uncoordinated interference appears. Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained, which indicates the interference subspaces can not be completely aligned.
Huiqin Du +3 more
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IEEE Transactions on Geoscience and Remote Sensing, 2019
Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks.
Y. Huang +5 more
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Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks.
Y. Huang +5 more
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A short note on the Frobenius norm of the commutator
Mathematical Notes, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wu, Yan-Dong, Liu, Xu-Qing
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Comments on "Is the Frobenius Matrix Norm Induced?" [with reply]
IEEE Transactions on Automatic Control, 2003In "Is the Frobenius matrix norm induced?", the authors ask whether the Frobenius and the H/sup 2/ norms are induced. There, they claimed that the Frobenius norm is not induced and, consequently, conjectured that the H/sup 2/ norm may not be induced. In this paper, it is shown that the Frobenius norm is induced on particular matrix spaces.
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Applied Mathematics and Computation, 2018
The iterative algorithm of a class of generalized coupled Sylvester-transpose matrix equations is presented. We prove that if the system is consistent, a solution can be obtained within finite iterative steps in the absence of round-off errors for any ...
Baohua Huang, Changfeng Ma
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The iterative algorithm of a class of generalized coupled Sylvester-transpose matrix equations is presented. We prove that if the system is consistent, a solution can be obtained within finite iterative steps in the absence of round-off errors for any ...
Baohua Huang, Changfeng Ma
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ISA transactions, 2019
Transient impulsive feature detection is of vital importance in fault diagnosis of rolling bearing. However, the transient impulsive feature of rolling bearing is always heavily buried in the noise contaminated signal, which makes it difficult to be ...
Kun Yu +5 more
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Transient impulsive feature detection is of vital importance in fault diagnosis of rolling bearing. However, the transient impulsive feature of rolling bearing is always heavily buried in the noise contaminated signal, which makes it difficult to be ...
Kun Yu +5 more
semanticscholar +1 more source
Frobenius norm-regularized robust graph learning for multi-view subspace clustering
Applied intelligence (Boston), 2022Shuqin Wang +3 more
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A Logarithmic Minimization Property of the Unitary Polar Factor in the Spectral and Frobenius Norms
SIAM Journal on Matrix Analysis and Applications, 2014The unitary polar factor $Q=U_p$ in the polar decomposition of $Z=U_p \, H$ is the minimizer over unitary matrices $Q$ for both $\|{\rm Log}(Q^* Z)\|^2$ and its Hermitian part $\|{{\rm sym}{_{_*}}\!}({\rm Log}(Q^* Z))\|^2$ over both $\mathbb{R}$ and $\mathbb{C}$ for any given invertible matrix $Z\in\mathbb{C}^{n\times n}$ and any matrix logarithm Log ...
Patrizio Neff +2 more
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Subspace segmentation with a Minimal Squared Frobenius Norm Representation.
2013We introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly.
Yu, Y, Wei, S
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