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   Articles with "matrix estimation" as a keyword



An Improving EFA for Clutter Suppression by Using the Persymmetric Covariance Matrix Estimation

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recommendations! Published in 2018 at "Circuits, Systems, and Signal Processing"

DOI: 10.1007/s00034-017-0743-y

Abstract: The extended factored approach (EFA) is believed to be one of the most efficient and practical space–time adaptive processing (STAP) algorithms for clutter suppression in an airborne radar system. However, it cannot effectively work in… read more here.

Keywords: clutter suppression; covariance matrix; persymmetric covariance; matrix estimation ... See more keywords
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FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs.

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recommendations! Published in 2022 at "Computer methods and programs in biomedicine"

DOI: 10.1016/j.cmpb.2022.106782

Abstract: BACKGROUND AND OBJECTIVE The fundamental matrix estimation is a classic problem in computer vision. The traditional algorithms require high-precision correspondences. However, correspondences in biplanar radiographs are difficult to match accurately. METHODS We propose an end-to-end… read more here.

Keywords: fundamental matrix; matrix; biplanar radiographs; network ... See more keywords

Methodology for O‐D matrix estimation using the revealed paths of floating car data on large‐scale networks

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recommendations! Published in 2020 at "Iet Intelligent Transport Systems"

DOI: 10.1049/iet-its.2019.0684

Abstract: The increasing availability of historical floating car data (FCD) represents a relevant chance to improve the accuracy of model-based traffic forecasting systems. A more precise estimation of origin–destination (O-D) matrices is a critical issue for… read more here.

Keywords: methodology; estimation; floating car; matrix estimation ... See more keywords

Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model

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recommendations! Published in 2018 at "Journal of the American Statistical Association"

DOI: 10.1080/01621459.2017.1340888

Abstract: ABSTRACT High-frequency financial data allow us to estimate large volatility matrices with relatively short time horizon. Many novel statistical methods have been introduced to address large volatility matrix estimation problems from a high-dimensional Itô process… read more here.

Keywords: high dimensional; estimation; high frequency; volatility ... See more keywords

Large Precision Matrix Estimation with Unknown Group Structure.

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recommendations! Published in 2024 at "Journal of the American Statistical Association"

DOI: 10.1080/01621459.2024.2442092

Abstract: Abstract The estimation of large precision matrices is crucial in modern multivariate analysis. Traditional sparsity assumptions, while useful, often fall short of accurately capturing the dependencies among features. This article addresses this limitation by focusing… read more here.

Keywords: precision; precision matrix; matrix estimation; group ... See more keywords

Fast and accurate inference of gene regulatory networks through robust precision matrix estimation

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recommendations! Published in 2022 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btac178

Abstract: MOTIVATION Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying Gene Regulatory Network (GRN), and reliably inferring such… read more here.

Keywords: gene regulatory; precision matrix; matrix estimation; robust precision ... See more keywords

Improved Large Dynamic Covariance Matrix Estimation With Graphical Lasso and Its Application in Portfolio Selection

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3031192

Abstract: The estimation of the large and high-dimensional covariance matrix and precision matrix is a fundamental problem in modern multivariate analysis. It has been widely applied in economics, finance, biology, social networks and health sciences. However,… read more here.

Keywords: covariance matrix; matrix estimation; large dynamic; covariance ... See more keywords

Discrete Mumford–Shah on Graph for Mixing Matrix Estimation

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recommendations! Published in 2019 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2019.2917518

Abstract: The discrete Mumford–Shah formalism has been introduced for the image denoising problem, allowing to capture both smooth behavior inside an object and sharp transitions on the boundary. In this letter, we propose first to extend… read more here.

Keywords: mumford shah; discrete mumford; mixing matrix; estimation ... See more keywords

Covariance Matrix Estimation Under Positivity Constraints With Application to Portfolio Selection

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recommendations! Published in 2022 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2022.3226117

Abstract: In this letter we propose a new method to estimate the covariance matrix under the constraint that its off-diagonal elements are non-negative, which has applications to portfolio selection in finance. We incorporate the non-negativity constraint… read more here.

Keywords: matrix estimation; covariance matrix; portfolio selection;

Noise Covariance Matrix Estimation in Block-Correlated Noise Field for Direction Finding

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recommendations! Published in 2025 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2025.3525898

Abstract: A noise covariance matrix estimation approach in unknown noise field for direction finding applicable for the practically important cases of nonuniform and block-diagonal sensor noise is proposed. It is based on an alternating procedure that… read more here.

Keywords: noise covariance; noise field; matrix estimation; direction finding ... See more keywords

Covariance Matrix Estimation From Correlated Sub-Gaussian Samples via the Shrinkage Estimator

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recommendations! Published in 2025 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2025.3541427

Abstract: Covariance matrix estimation is of great importance in statistical signal processing. This paper considers covariance matrix estimation from correlated complex sub-Gaussian samples via the shrinkage estimator. We establish non-asymptotic error bounds for this estimator in… read more here.

Keywords: covariance matrix; shrinkage estimator; matrix estimation; estimator ... See more keywords