Results 21 to 30 of about 147,187 (215)
Analysis of the covariance matrix in FWI through density of covariance maps
CT&F Ciencia, Tecnología & Futuro, 2020 Full waveform inversion (FWI) is a tool for the inversion of seismic data. There are several sources of uncertainty in the results provided by FWI. The quantification of such uncertainties has been studied through the resolution matrix (Res), which rests
Anyeres Neider Jimenez +3 more
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IET Radar, Sonar & Navigation, 2023 Benefit from the transmission diversity smoothing (TDS) effect upon coherent targets decorrelation, the kind of adaptive beamformers can be directly applied for multiple‐input multiple‐output (MIMO) sonar applications.
Kuan Fan, Xionghou Liu, Chao Sun
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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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Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix
Leida xuebao, 2017 In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix.
Yu Han +3 more
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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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Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data
Separations, 2021 Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features.
Adam Mieldzioc +2 more
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High-Dimensional Covariance Estimation via Constrained Lq-Type Regularization
Mathematics, 2023 High-dimensional covariance matrix estimation is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields.
Xin Wang +3 more
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A covariance matrix test for high-dimensional data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2016 For the multivariate normally distributed data with the dimension larger than or equal to the number of observations, or the sample size, called high-dimensional normal data, we proposed a test for testing the null hypothesis that the covariance matrix
Saowapha Chaipitak, Samruam Chongcharoen
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Molecular Oncology, EarlyView.This study integrates publicly available transcriptomic datasets to identify molecular signatures associated with response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. By analyzing a combination of multiple cohorts with bioinformatics approaches, we reveal biological pathways and immune‐related features that may improve ...
Aleksandra Stanojevic +10 more
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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
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