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Elliptically Symmetric Distributions in Signal Processing and Machine Learning

This book constitutes a review of recent developments in the theory and practical exploitation of the elliptical model for measured data in both classical and emerging areas of signal processing. It develops techniques usable in (among other areas): graph learning, robust clustering, linear shrinkag...

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Korporativní autor: SpringerLink (Online service)
Další autoři: Delmas, Jean-Pierre (Editor), El Korso, Mohammed Nabil (Editor), Fortunati, Stefano (Editor), Pascal, Frédéric (Editor)
Médium: Elektronický zdroj E-kniha
Jazyk:angličtina
Vydáno: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Vydání:1st ed. 2024.
Témata:
Signal processing.
Statistics .
Digital and Analog Signal Processing.
Statistical Theory and Methods.
On-line přístup:https://doi.org/10.1007/978-3-031-52116-4
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Obsah:
  • 1. Background on real and complex elliptically symmetric distributions
  • Part I: Theoretical developments
  • 2.The Fisher-Rao geometry of CES distributions
  • 3. Linear shrinkage of sample covariance matrix or matrices under elliptical distributions: a review
  • 4. Robust estimation with missing values for elliptical distributions
  • Part II: Performance analysis
  • 5. Semiparametric estimation in elliptical distributions
  • 6. Estimation and Detection Under Misspecification and Complex Elliptically Symmetric Distributions
  • 7. Performance analysis of subspace-based algorithms in CES data models
  • Part III: Applications to machine learning
  • 8. Robust Bayesian Cluster Enumeration for RES Distributions
  • 9. FEMDA: a unified framework for discriminant analysis
  • 10. Learning Graphs from Heavy-tailed Data.

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