Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models by Daria Bystrova, Daria Bystrova, Giovanni Poggiato, Giovanni Poggiato, Billur Bektaş, Julyan Arbel, James S. Clark, James S. Clark, James S. Clark, Alessandra Guglielmi, Wilfried Thuiller

“…Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. …”
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Eigen Values of Covariance Matrix for Feature Extraction of Latin Printed Image by khalil I. Alsaif, Shaimaa M.Mohi Al-Deen

“…In this research the covariance matrix which was used in so many fields, its eigen values adopted to be the main parameters for Latin printed character recognition. …”
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Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations by Chen, Jie, Cao, Nannan, Low, Kian Hsiang, Ouyang, Ruofei, Colin Keng-Yan, Tan, Jaillet, Patrick

“…This paper presents two parallel GP regression methods that exploit low-rank covariance matrix approximations for distributing the computational load among parallel machines to achieve time efficiency and scalability. …”
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The effect on cosmological parameter estimation of a parameter dependent covariance matrix by Kodwani, D, Alsono, D, Ferreira, PG

“…Cosmological large-scale structure analyses based on two-point correlation functions often assume a Gaussian likelihood function with a fixed covariance matrix. We study the impact on cosmological parameter estimation of ignoring the parameter dependence of this covariance matrix, focusing on the particular case of joint weak-lensing and galaxy clustering analyses. …”

Dual-feature spectrum sensing exploiting eigenvalue and eigenvector of the sampled covariance matrix by Yanping Chen, Yulong Gao

“…The signal can be charactered by both eigenvalues and eigenvectors of covariance matrix. However, the existing detection methods only exploit the eigenvalue or eigenvector. …”
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Structural constraints on the covariance matrix derived from multiple aligned protein sequences. by William R Taylor, Michael I Sadowski

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Adaptive Exploration through Covariance Matrix Adaptation Enables Developmental Motor Learning by Stulp Freek, Oudeyer Pierre-Yves

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A Covariance Matrix Reconstruction Approach for Single Snapshot Direction of Arrival Estimation by Murdifi Muhammad, Minghui Li, Qammer Abbasi, Cindy Goh, Muhammad Ali Imran

“…This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. …”
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Robust Range Ambiguous Deceptive Target Suppression Based on Covariance Matrix Reconstruction by Zhuang Xie, Jiahua Zhu, Chongyi Fan, Xiaotao Huang, Jian Wang

“…First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. …”
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Initialization of SINS/GNSS Error Covariance Matrix Based on Error States Correlation by Jun Tang, Hongwei Bian, Heng Ma, Rongying Wang

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Single-Pass Covariance Matrix Calculation on a Hybrid FPGA/CPU Platform by Arnold Lukas On, Owaida Muhsen

“…Based on a novel decomposition of the covariance matrix, a design that requires only one pass of data for calculating the covariance matrix is presented. …”
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Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy by Guoquan Chen, Jian Yin, Shenhua Yang

“…In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. …”
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PolSAR Ship Detection Based on Azimuth Sublook Polarimetric Covariance Matrix by Ziyuan Yang, Lu Fang, Biao Shen, Tao Liu

Subjects: “…sublook polarimetric covariance matrix (SPCM)…”
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Generalized background error covariance matrix model (GEN_BE v2.0) by G. Descombes, T. Auligné, F. Vandenberghe, D. M. Barker, J. Barré

“…In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (<b>B</b>) are usually too large to be explicitly determined and <b>B</b> needs to be modeled. …”
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Virtual covariance matrix reconstruction-based adaptive beamforming for small aperture array. by Lin Chang, Hao Zhang, Hua Yang, Tingting Lv, Ning Tang

“…The first method employs an integration algorithm that combines angular sector and gradient vector search to reconstruct the interference covariance matrix (ICM). Then, the interference-plus-noise covariance matrix (INCM) is reconstructed combined with the estimated noise power. …”
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A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition by Yantao Li, Lian Xue, Huafeng Qin, Lan Qin

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Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix by Yu Han, Shui Penglang, Yang Chunjiao, Shi Sainan

Subjects: “…Covariance matrix estimation…”
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A study of covariance matrix estimators for Markowitz mean-variance portfolio optimization by Luo, Yun

“…This paper aims to compare the performance of 3 covariance matrix estimators with respect to sample covariance matrix in terms of portfolio optimisation using historical return data of 30 top stocks traded at Singapore market from May 2012 to October 2014. …”
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MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization. by Woolrich, M, Hunt, L, Groves, A, Barnes, G

“…A key ingredient in a beamformer is the estimation of the data covariance matrix. When the noise levels are high, or when there is only a small amount of data available, the data covariance matrix is estimated poorly and the signal-to-noise ratio (SNR) of the beamformer output degrades. …”

Approximate asymptotic variance-covariance matrix for the whittle estimators of GAR(1) parameters by Shitan, Mahendran, Peiris, Shelton

“…This article derives approximate theoretical expressions for the enteries of the asymptotic variance-covariance matrix for those estimates of GAR(1) parameters. …”
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