Results 31 to 40 of about 164,304 (297)
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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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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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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The matrix completion problem [PDF]
, 2022 Nowadays, the relevant amount of information exchanges force the experts to create more reliable, secure and error-less devices through which these can happen.
Nalin, Gianmarco
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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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Computing the nearest euclidean distance matrix with low embedding dimensions [PDF]
, 2013 Euclidean distance embedding appears in many high-profile applications including wireless sensor network localization, where not all pairwise distances among sensors are known or accurate.
Qi, Hou-Duo, Yuan, Xiaoming, Qi, Hou Duo
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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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Imputation of quantitative genetic interactions in epistatic MAPs by interaction propagation matrix completion [PDF]
, 2014 A popular large-scale gene interaction discovery platform is the Epistatic Miniarray Profile (E-MAP). E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions.
Marinka Žitnik +3 more
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Maximum entropy methods, covariance completion and applications [PDF]
, 2022 The aim of this thesis is to give an introduction to the maximum entropy methods in model fitting, focusing on the general framework proposed by Edwin T. Jaynes in 1957. Subsequently, Arthur P.
Barbiero, Luca
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