Description

Book Synopsis

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 Misspeci?cation 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.

Elliptically Symmetric Distributions in Signal Processing and Machine Learning

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    A Hardback by Jean-Pierre Delmas

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      View other formats and editions of Elliptically Symmetric Distributions in Signal Processing and Machine Learning by Jean-Pierre Delmas

      Publisher: Springer
      Publication Date: 12/10/2024
      ISBN13: 9783031521157, 978-3031521157
      ISBN10:
      Also in:
      Electronics engineering

      Description

      Book Synopsis

      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 Misspeci?cation 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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