Results 21 to 30 of about 158,513 (260)
A Nonconvex Method to Low-Rank Matrix Completion
IEEE Access, 2022 In recent years, the problem of recovering a low-rank matrix from partial entries, known as low-rank matrix completion problem, has attracted much attention in many applications.
Haizhen He +3 more
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Conformalized matrix completion
Advances in Neural Information Processing Systems 36, 2023 Matrix completion aims to estimate missing entries in a data matrix, using the assumption of a low-complexity structure (e.g., low rank) so that imputation is possible. While many effective estimation algorithms exist in the literature, uncertainty quantification for this problem has proved to be challenging, and existing methods are extremely ...
Yu Gui, Rina Barber, Cong Ma 0001
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Robust Global Motion Estimation with Matrix Completion [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 In this paper we address the problem of estimating the attitudes and positions of a set of cameras in an external coordinate system. Starting from a conventional global structure-from-motion pipeline, we present some substantial advances.
F. Arrigoni +4 more
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Deep Sparse Depth Completion Using Multi-Affinity Matrix
IEEE Access, 2023 Image-guided depth completion aims to generate dense depth maps from sparse depth maps guided by their corresponding color (RGB) images. In this paper, we propose deep sparse depth completion using multi-affinity matrix.
Wei Zhao, Cheolkon Jung, Jaekwang Kim
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Local low-rank approach to nonlinear matrix completion
EURASIP Journal on Advances in Signal Processing, 2021 This paper deals with a problem of matrix completion in which each column vector of the matrix belongs to a low-dimensional differentiable manifold (LDDM), with the target matrix being high or full rank.
Ryohei Sasaki +3 more
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Robust matrix completion [PDF]
Probability Theory and Related Fields, 2016 This paper considers the problem of recovery of a low-rank matrix in the situation when most of its entries are not observed and a fraction of observed entries are corrupted. The observations are noisy realizations of the sum of a low rank matrix, which we wish to recover, with a second matrix having a complementary sparse structure such as element ...
Klopp, Olga +2 more
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Correntropy Based Matrix Completion
Entropy, 2018 This paper studies the matrix completion problems when the entries are contaminated by non-Gaussian noise or outliers. The proposed approach employs a nonconvex loss function induced by the maximum correntropy criterion.
Yuning Yang +2 more
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Orthogonal Inductive Matrix Completion [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 To appear in Transactions of Neural Networks and Learning Systems (TNNLS)
Antoine Ledent +2 more
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Low-Rank Matrix Completion: A Contemporary Survey
IEEE Access, 2019 As a paradigm to recover unknown entries of a matrix from partial observations, low-rank matrix completion (LRMC) has generated a great deal of interest.
Luong Trung Nguyen +2 more
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Targeted matrix completion [PDF]
, 2017 Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods depends on the number of observed
Natali Ruchansky +2 more
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