Results 41 to 50 of about 615,565 (289)
Missing Covariance Matrix Recovery with the FDA-MIMO Radar Using Deep Learning Method
Leida xuebao, 2023 The realization of anti-jamming technologies via beamforming for applications in Frequency-Diverse Arrays and Multiple-Input and Multiple-Output (FDA-MIMO) radar is a field that is undergoing intensive research.
Zihang DING, Junwei XIE, Bo WANG
doaj +1 more source
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
core +3 more sources
Covariance beamforming, covariance matrix tapers and matrix beamforming are related
Electronics Letters, 2008 It is shown that the covariance beamforming, covariance matrix tapers and matrix beamforming approaches, which were considered separately from one another in the previous array processing literature, are in fact related. The relationships between them in terms of both generality and design procedures are clarified.
J. Li, P. Stoica, T. Yardibi
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Robust high-dimensional precision matrix estimation
, 2015 The dependency structure of multivariate data can be analyzed using the covariance matrix $\Sigma$. In many fields the precision matrix $\Sigma^{-1}$ is even more informative.
B. Bertsekas +24 more
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Massive data compression for parameter-dependent covariance matrices [PDF]
, 2017 We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated datasets that are required to estimate the covariance matrix required for the analysis of gaussian-distributed data.
de Mijolla, Damien +3 more
core +2 more sources
Asset Allocation Strategies Using Covariance Matrix Estimators
Acta Universitatis Sapientiae: Economics and Business, 2022 The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high ...
László PáL
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Perturbative approach to covariance matrix of the matter power spectrum
, 2016 We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations.
Mohammed, Irshad +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
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Adaptive detection with bounded steering vectors mismatch angle [PDF]
, 2007 We address the problem of detecting a signal of interest (SOI), using multiple observations in the primary data, in a background of noise with unknown covariance matrix.
Besson, Olivier
core +1 more source
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
openaire +1 more source