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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| Materyal Türü: | Elektronik Ekitap |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edisyon: | 1st ed. 2024. |
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| Online Erişim: | https://doi.org/10.1007/978-3-031-52116-4 |
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| 245 | 1 | 0 | |a Elliptically Symmetric Distributions in Signal Processing and Machine Learning |h [electronic resource] / |c edited by Jean-Pierre Delmas, Mohammed Nabil El Korso, Stefano Fortunati, Frédéric Pascal. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XIV, 376 p. 76 illus.). |b 63 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
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| 505 | 0 | |a 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. | |
| 520 | |a 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 shrinkage, information geometry, subspace-based algorithm design, and semiparametric and misspecified estimation. The various contributions combine to show how the goal of inferring information from a set of acquired data, recurrent in statistical signal processing, can be achieved, even when the common practical assumption of Gaussian distribution in the data is not valid. The elliptical model propounded maintains the performance of its inference procedures even when that assumption fails. The elliptical distribution, being fully characterized by its location vector, its scatter/covariance matrix and its so-called density generator, used to describe the impulsiveness of the data, is sufficiently flexible to model heterogeneous applications. This book is of interest to any graduate students and academic researchers wishing to acquaint themselves with the latest research in an area of rising consequence. It is also of assistance to practitioners working in data analysis, wireless communications, radar, and image processing. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Statistics . | |
| 650 | 1 | 4 | |a Digital and Analog Signal Processing. |
| 650 | 2 | 4 | |a Statistical Theory and Methods. |
| 700 | 1 | |a Delmas, Jean-Pierre. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a El Korso, Mohammed Nabil. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Fortunati, Stefano. |e editor. |0 (orcid)0000-0003-0273-4510 |1 https://orcid.org/0000-0003-0273-4510 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Pascal, Frédéric. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031521157 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031521171 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031521188 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-52116-4 |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||