Results 51 to 60 of about 3,678,430 (211)
Matrix completion discriminant analysis [PDF]
Computational Statistics & Data Analysis, 2015 Matrix completion discriminant analysis (MCDA) is designed for semi-supervised learning where the rate of missingness is high and predictors vastly outnumber cases. MCDA operates by mapping class labels to the vertices of a regular simplex. With c classes, these vertices are arranged on the surface of the unit sphere in c - 1 dimensional Euclidean ...
Wu, Tong Tong, Lange, Kenneth
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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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Nonconvex matrix completion with Nesterov’s acceleration
Big Data Analytics, 2018 Background In matrix completion fields, the traditional convex regularization may fall short of delivering reliable low-rank estimators with good prediction performance. Previous works use the alternation least squares algorithm to optimize the nonconvex
Xiao-Bo Jin +4 more
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Matrix Completion With Noise [PDF]
Proceedings of the IEEE, 2009 On the heels of compressed sensing, a new field has very recently emerged. This field addresses a broad range of problems of significant practical interest, namely, the recovery of a data matrix from what appears to be incomplete, and perhaps even ...
E. Candès, Y. Plan
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Binary Matrix Completion With Nonconvex Regularizers
IEEE Access, 2019 Many practical problems involve the recovery of a binary matrix from partial information, so the binary matrix completion (BMC) technique has increasingly been of interest in machine learning. In particular, we consider a special case of the BMC problems,
Chunsheng Liu, Hong Shan
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High-Performance Homomorphic Matrix Completion on Multiple GPUs
IEEE Access, 2020 In various applications such as trajectory tracking in mobile social networks and online recommendation systems, the massive raw data are often incomplete due to various unpredictable or unavoidable reasons. Matrix completion algorithms are effective for
Tao Zhang, Han Lu, Xiao-Yang Liu
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Adaptive multinomial matrix completion
Electronic Journal of Statistics, 2015 The task of estimating a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Most works on matrix completion have focused on recovering an unknown real-valued low-rank matrix from a random sample of its entries. Here, we investigate the case of highly quantized observations when the measurements can take only a ...
Klopp, Olga +3 more
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Anomaly-Tolerant Traffic Matrix Estimation via Prior Information Guided Matrix Completion
IEEE Access, 2017 Network management, planning, and optimization rely on accurate and complete traffic measurement. However, anomalies and missing data are inevitable in direct traffic measurement because of high measurement costs and unreliable network transport ...
Wencai Ye +4 more
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A regularised deep matrix factorised model of matrix completion for image restoration
IET Image Processing, 2022 It has been an important approach of using matrix completion to perform image restoration. Most previous works on matrix completion focus on the low‐rank property by imposing explicit constraints on the recovered matrix, such as the constraint of the ...
Zhemin Li +3 more
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Accelerated Path Tracing With GAN and Matrix Completion
IEEE Access, 2021 Denoising-based techniques have recently been shown to be effective for accelerating path tracing rendering methods. However, there remains a problem which is input images need the minimum necessary samples number in order to ensure the quality of the ...
Qiwei Xing, Chunyi Chen, Zhihua Li
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