Results 251 to 260 of about 953,917 (266)
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Pattern Recognition, 2017
Abstract In this paper, we consider the problem of linear dimensionality reduction with the novel technique of low-rank representation, which is a promising tool of discovering subspace structures of given data. Existing approaches based on graph embedding usually capture structure of data via stacking the local structure of each datum, such as ...
Yupei Zhang, Ming Xiang, Bo Yang 0041
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Abstract In this paper, we consider the problem of linear dimensionality reduction with the novel technique of low-rank representation, which is a promising tool of discovering subspace structures of given data. Existing approaches based on graph embedding usually capture structure of data via stacking the local structure of each datum, such as ...
Yupei Zhang, Ming Xiang, Bo Yang 0041
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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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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 CP Rank and Tucker Rank Tensor Completion for Estimating Missing Components in Image Data
IEEE Transactions on Circuits and Systems for Video Technology, 2020Yipeng Liu, Zhen Long, Huyan Huang
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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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Bayesian Low-Tubal-Rank Robust Tensor Factorization with Multi-Rank Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Yang Zhou
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From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
IEEE Transactions on Image Processing, 2020Zhiyuan Zha, Xin Yuan, Bihan Wen
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Accurate Tensor Completion via Adaptive Low-Rank Representation
IEEE Transactions on Neural Networks and Learning Systems, 2020Wei Wei, Qinfeng Shi, Chunhua Shen
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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
SIAM Review, 2010Benjamin Recht +2 more
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