Results 281 to 290 of about 14,259,947 (309)
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2012
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation.
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Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation.
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IEEE Transactions on Signal Processing, 1996
We introduce a class of adaptive filters based on sequential adaptive eigendecomposition (subspace tracking) of the data covariance matrix. These new algorithms are completely rank revealing, and hence, they can perfectly handle the following two relevant data cases where conventional recursive least squares (RLS) methods fail to provide satisfactory ...
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We introduce a class of adaptive filters based on sequential adaptive eigendecomposition (subspace tracking) of the data covariance matrix. These new algorithms are completely rank revealing, and hence, they can perfectly handle the following two relevant data cases where conventional recursive least squares (RLS) methods fail to provide satisfactory ...
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Low-rank physical model recovery from low-rank signal approximation
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017This work presents a mathematical approach for recovering a physical model from a low-rank approximation of measured data obtained via the singular value decomposition (SVD). The general form of a low-rank physical model of the data is often known, so the presented approach learns the proper rotation and scaling matrices from the singular vectors and ...
Charles Ethan Hayes +2 more
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Improvement of the Low Rank Attack
2010 International Symposium On Information Theory & Its Applications, 2010Time complexity of Low Rank Attack is lower than originally estimated. Now the algorithm is improved and the time complexity is computed as O(Ln3 + mn4), outperforming the original Low Rank Attack with the complexity O(Ln3 qr +mn4).
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Low-Rank Tensor Completion Method for Implicitly Low-Rank Visual Data
IEEE Signal Processing Letters, 2022Teng-Yu Ji, Xi-Le Zhao, Dong-Lin Sun
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A Riemannian rank-adaptive method for low-rank matrix completion
Computational Optimization and Applications, 2021Bin Gao, P -A Absil
exaly
Low CP Rank and Tucker Rank Tensor Completion for Estimating Missing Components in Image Data
IEEE Transactions on Circuits and Systems for Video Technology, 2020Ce Zhu, Zhen Long, Huyan Huang
exaly
Recent developments in drying and dewatering for low rank coals
Progress in Energy and Combustion Science, 2015Congliang Huang +2 more
exaly
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
IEEE Transactions on Image Processing, 2020Ce Zhu, Jiachao Zhang, Zhiyuan Zha
exaly
Low-Rank Transformer for High-Resolution Hyperspectral Computational Imaging
International Journal of Computer VisionYuanye Liu, Renwei Dian, Shutao Li
semanticscholar +1 more source