Results 21 to 30 of about 164,304 (297)
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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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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Matrix completion via modified schatten 2/3-norm
EURASIP Journal on Advances in Signal Processing, 2023 Low-rank matrix completion is a hot topic in the field of machine learning. It is widely used in image processing, recommendation systems and subspace clustering.
Jincai Ha +3 more
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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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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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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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