Results 11 to 20 of about 158,513 (260)

Poisson matrix completion [PDF]

2015 IEEE International Symposium on Information Theory (ISIT), 2015
Submitted to IEEE for ...
Yang Cao 0013, Yao Xie 0002
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Matrix Completion with Queries [PDF]

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data ...
Natali Ruchansky, Mark Crovella, Evimaria Terzi   +2 more
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Categorical matrix completion [PDF]

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
We consider the problem of completing a matrix with categorical-valued entries from partial observations. This is achieved by extending the formulation and theory of one-bit matrix completion. We recover a low-rank matrix $X$ by maximizing the likelihood ratio with a constraint on the nuclear norm of $X$, and the observations are mapped from entries of
Yang Cao 0013, Yao Xie 0002
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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, Chuan Li, Xiaohu Luo, Zhi Wang   +3 more
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Matrix Completion Based on Low-Rank and Local Features Applied to Images Recovery and Recommendation Systems

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, Kai Xu, Songfeng Liang, Chen Zhao   +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, Andrea Montanari   +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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Causal Matrix Completion

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, Munther A. Dahleh, Devavrat Shah, Dennis Shen   +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, Xiaohong Gu, Conghua Zhou, Xiaolong Zhu, Yi Jiang, John Kingsley Arthur, Eric Appiah Mantey, Ernest Domanaanmwi Ganaa   +7 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, Samuel M. Mercier, Ismail Uysal   +2 more
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