Results 51 to 60 of about 2,877,467 (378)
Matrix Completion With Covariate Information
Journal of the American Statistical Association, 2018 This paper investigates the problem of matrix completion from corrupted data, when additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A0. Given a covariate matrix X with
Xiaojun Mao +2 more
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Over-sampling imbalanced datasets using the Covariance Matrix [PDF]
EAI Endorsed Transactions on Energy Web, 2020 INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority ...
Ireimis Leguen-deVarona +3 more
doaj +1 more source
Physical properties of the Schur complement of local covariance matrices [PDF]
, 2007 General properties of global covariance matrices representing bipartite Gaussian states can be decomposed into properties of local covariance matrices and their Schur complements. We demonstrate that given a bipartite Gaussian state $\rho_{12}$ described
Eisert J Wolf M M +7 more
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Estimating the power spectrum covariance matrix with fewer mock samples [PDF]
, 2015 The covariance matrices of power-spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues ...
Pearson, David W., Samushia, Lado
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Weighted covariance matrix estimation [PDF]
Computational Statistics & Data Analysis, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
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Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance.
P. Li +3 more
semanticscholar +1 more source
Electronics Letters, 2021 The existing secret key generation (SKG) techniques are not applicable for frequency division duplex (FDD) Internet of Things networks due to the low power constraints and limited computing resources.
Zheng Wan +3 more
doaj +1 more source
DOA-Estimation Method Based on Improved Spatial-Smoothing Technique
Mathematics, 2023 To improve the data utilization of the sensor array and direction-of-arrival-(DOA)-estimation performance for coherent signals, a DOA-estimation method with a modified spatial-smoothing technique is proposed. The covariance matrix of the received data of
Yujun Hou +4 more
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Estimating Mean and Covariance Structure with Reweighted Least Squares [PDF]
, 2020 Does Reweighted Least Squares (RLS) perform better in small samples than maximum likelihood (ML) for mean and covariance structure? ML statistics in covariance structure analysis are based on the asymptotic normality assumption; however, actual ...
Zheng, Bang Quan
core
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices [PDF]
, 2015 This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
Cai, Tony, Ma, Zongming, Wu, Yihong
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