Results 11 to 20 of about 2,091,920 (156)
N-matrix completion problem [PDF]
Linear Algebra and its Applications, 2003 An \(n \times n\) matrix is called an \(N\)-matrix if all principal minors are negative. The authors prove that a combinatorially symmetric partial \(N\)-matrix has an \(N\)-matrix completion if the graph of its specified entries is a 1-chordal graph.
Araújo, C. Mendes +2 more
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IEEE Access, 2022 Recent advances have shown that the challenging problem of matrix completion arises from real-world applications, such as image recovery, and recommendation systems.
Ying Zhang +3 more
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Regularization for matrix completion [PDF]
2010 IEEE International Symposium on Information Theory, 2010 We consider the problem of reconstructing a low rank matrix from noisy observations of a subset of its entries. This task has applications in statistical learning, computer vision, and signal processing. In these contexts, "noise" generically refers to any contribution to the data that is not captured by the low-rank model.
Raghunandan H. Keshavan +1 more
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Deep Matrix Factorization Based on Convolutional Neural Networks for Image Inpainting
Entropy, 2022 In this work, we formulate the image in-painting as a matrix completion problem. Traditional matrix completion methods are generally based on linear models, assuming that the matrix is low rank.
Xiaoxuan Ma, Zhiwen Li, Hengyou Wang
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A Novel Hierarchical Deep Matrix Completion Method
IEEE Access, 2021 The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications.
Yaru Chen +7 more
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CoRR, 2021 Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are "missing completely at random" (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability.
Anish Agarwal +3 more
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Self-Supervised Feature Specific Neural Matrix Completion
IEEE Access, 2020 Unsupervised matrix completion algorithms mostly model the data generation process by using linear latent variable models. Recently proposed algorithms introduce non-linearity via multi-layer perceptrons (MLP), and self-supervision by setting separate ...
Mehmet Aktukmak +2 more
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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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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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