Results 31 to 40 of about 44,247 (253)
HOSVD-Based Algorithm for Weighted Tensor Completion
Journal of Imaging, 2021 Matrix completion, the problem of completing missing entries in a data matrix with low-dimensional structure (such as rank), has seen many fruitful approaches and analyses.
Zehan Chao +2 more
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Lower bounds for the low-rank matrix approximation
Journal of Inequalities and Applications, 2017 Low-rank matrix recovery is an active topic drawing the attention of many researchers. It addresses the problem of approximating the observed data matrix by an unknown low-rank matrix. Suppose that A is a low-rank matrix approximation of D, where D and A
Jicheng Li, Zisheng Liu, Guo Li
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Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
IEEE Open Access Journal of Power and Energy, 2021 There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows
Meng Wang +6 more
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Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
Algorithms, 2016 Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields
Jiarong Shi +3 more
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A New Estimation Algorithm for Frequency and Amplitude in Harmonic Signal Processing
IEEE Access, 2020 Low-rank matrix recovery is a large-scale data analysis and processing technology; its related theory has been widely used in image restoration, image denoising, video background modeling, signal recovery and other fields. This paper proposes an improved
Yanbo Mai +4 more
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Hybrid Matrix Completion Model for Improved Images Recovery and Recommendation Systems
IEEE Access, 2021 Matrix completion methods have been widely applied in images recovery and recommendation systems. Most of them are only based on the low-rank characteristics of matrices to predict the missing entries.
Kai Xu +4 more
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Rank Awareness in Group-Sparse Recovery of Multi-Echo MR Images
Sensors, 2013 This work addresses the problem of recovering multi-echo T1 or T2 weighted images from their partial K-space scans. Recent studies have shown that the best results are obtained when all the multi-echo images are reconstructed by simultaneously exploiting
Rabab Ward, Angshul Majumdar
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Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery
, 2022 We revisit the problem of recovering a low-rank positive semidefinite matrix from rank-one projections using tools from optimal transport. More specifically, we show that a variational formulation of this problem is equivalent to computing a Wasserstein barycenter.
Tyler Maunu +2 more
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The augmented lagrange multipliers method for matrix completion from corrupted samplings with application to mixed Gaussian-impulse noise removal. [PDF]
PLoS ONE, 2014 This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning ...
Fan Meng, Xiaomei Yang, Chenghu Zhou
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Rapid Gradient Descent Method for Low-Rank Matrix Recovery
MathematicsIn this paper, we present a rapid gradient descent method for solving low-rank matrix recovery problems. Our method extends the conventional gradient descent framework by exploiting the problem’s unique features to develop an innovative fast gradient ...
Yujing Zhang, Peng Wang, Detong Zhu
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