Averaging Symmetric Positive-Definite Matrices

Xinru Yuan (), Wen Huang (), Pierre-Antoine Absil () and Kyle A. Gallivan ()
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Xinru Yuan: Florida State University, Department of Mathematics
Wen Huang: Xiamen University, School of Mathematical Sciences
Pierre-Antoine Absil: Université catholique de Louvain, Department of Mathematical Engineering, ICTEAM Institute
Kyle A. Gallivan: Florida State University, Department of Mathematics

Chapter Chapter 20 in Handbook of Variational Methods for Nonlinear Geometric Data, 2020, pp 555-575 from Springer

Abstract: Abstract Symmetric positive definite (SPD) matrices have become fundamental computational objects in many areas, such as medical imaging, radar signal processing, and mechanics. For the purpose of denoising, resampling, clustering or classifying data, it is often of interest to average a collection of symmetric positive definite matrices. This paper reviews and proposes different averaging techniques for symmetric positive definite matrices that are based on Riemannian optimization concepts.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-31351-7_20

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DOI: 10.1007/978-3-030-31351-7_20

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