Averaging Symmetric Positive-Definite Matrices

Yuan, Xinru;Huang, Wen;Absil, Pierre-Antoine;Gallivan, Kyle A.
(2019) Hanbook of Variational Methods for Nonlinear Geometric Data and applications — ISBN: [978-3-030-31350-0], p. 555-575, published

Files

ASPDM_techrep.pdf
  • Open Access
  • Adobe PDF
  • 362.23 KB
Download

Details

Authors
  • Yuan, XinruDepartment of Mathematics, Florida State University, Tallahassee FL 32306-4510, USA.
    Author
  • Huang, WenSchool of Mathematical Sciences, Xiamen University, P.R.China.
    Author
  • Absil, Pierre-AntoineUCLouvain
    Author
  • Gallivan, Kyle A.Department of Mathematics, Florida State University, Tallahassee FL 32306-4510, USA.
    Author
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.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Yuan, X., Huang, W., Absil, P.-A., & Gallivan, K. A. (2019). Averaging Symmetric Positive-Definite Matrices. In Grohs, Philipp ; Holler, Martin ; Weinmann, Andreas (ed.), Hanbook of Variational Methods for Nonlinear Geometric Data and applications (p. p. 555-575). Springer handbooks. https://doi.org/10.1007/978-3-030-31351-7_20