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 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.

Jean-Pierre Delmas received the engineering degree from Ecole Centrale de Lyon, France in 1973, the Certificat d’Etudes Supérieures from Ecole Nationale Supérieure des Télécommunications, Paris, France in 1982 and the Habilitation à diriger des recherches degree from the University of Paris XI, Orsay, France in 2001. Since 1980, he has been with Telecom SudParis where he is currently a Professor with the CITI department. He was the deputy director (2005-2010) and the director (2011-2014) of UMR 5157 (CNRS laboratory). His teaching and research interest lie in statistical methods for signal processing with emphasis on asymptotic performance analysis and array processing applied to multi-sensor systems in the context of communications. He is author or co-author of more than 140 publications (journal, conference and chapter of book, book). He was an Associate Editor for the IEEE Transactions on Signal Processing (2002-2006) and (2010-2014) for Signal Processing (Elsevier) (2009-2020), and currently for IEEE Signal Processing Letters. From 2011 to 2016, he was a member of the IEEE Sensor Array and Multichannel Technical Committee.

Mohammed Nabil El Korso received the M.Sc. in Electrical Engineering from the National Polytechnic School, Algeria in 2007. He obtained the Master Research degree in Signal and Image Processing from ParisSud XI University, France in 2008. In 2011, he obtained his Ph.D. degree from Paris-Sud XI University. From 2011 to 2012, he was a research scientist in the Communication Systems Group at Technische Universitat Darmstadt, Germany. He was Assistant Professor at Ecole Normale Supérieure de Cachan from 2012 to 2013, and Assistant Professor at University of Paris Nanterre from 2013 to 2022. Currently, he is Professor at Paris Saclay University. His research interests include robust statistical signal processing, statistical analysis with missing values, estimation with mixed effects models with application to radio-interferometry, SAR and array processing. Prof. M. N. El Korso is Associate Editor for IEEE Transactions for Signal Processing, Handling Editor for Signal Processing journal (Elsevier), since 2019, and he was an Associate Editor for Digital Signal Processing (Elsevier) between 2019-2022 and for the IEEE Access between 2019-2020 and Guest Editor for a special issue of Signal Processing in 2020. He is member of the EURASIP TMTSP TAC (Theoretical and Methodological Trends in Signal Processing) and the EURASIP SPMuS TAC (Signal Processing for Multi-sensor Systems).

Stefano Fortunati received the graduate degree in telecommunication engineering and the Ph.D. degree, both from the University of Pisa, Italy, in 2008 and 2012, respectively. In 2012, he joined the Department of Ingegneria dell’Informazione, University of Pisa, where he was a researcher with a postdoc position until September 2019. Since October 2019, he is an associate researcher at Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systems (L2S), 91190, Gif-sur-Yvette, France. From Sept. 2020 he is a permanent lecturer (enseignant-chercheur) at IPSA in the Parisian campus of Ivry-sur-Seine. From September 2012 to November 2012 and from September 2013 to November 2013, he was a Visiting Researcher with the CMRE NATO Research Center, La Spezia, Italy. From May 2017 to April 2018, he spent a period of one year as a Visiting Researcher with the Signal Processing Group, Technische Universität Darmstadt. His professional expertise encompasses different areas of the statistical signal processing and applied statistics, with particular focus on point estimation and hypothesis testing, performance bounds, misspecification theory, robust and semiparametric statistics and statistical learning theory.

Frédéric Pascal received the Master’s degree in Applied Statistics (University Paris-Jussieu, 2003), the Ph.D. degree of Signal Processing (University Paris-Nanterre, 2006), and the Research Directorship Habilitation (HDR) thesis in Signal Processing (Universty Paris-Sud, 2012). From Jan. 2014, Frédéric Pascal is a full Professor in the L2S laboratory at CentraleSupélec, University Paris-Saclay. In 2013/2014, he was a visiting associate professor in the ECE department at the National University of Singapore. He is also the coordinator of Artificial Intelligence activities at CentraleSupélec and the director of the DATAIA institute, first and most important French ecosystem in AI and data science. His research interests contain estimation, detection and classification for statistical signal processing and applications. He is the author/coauthor of more than one hundred fifty papers in the top journals and conferences in Signal Processing, Image Processing and Statistics/Machine Learning.