Michael Fauß, Michael Muma and Abdelhak M. Zoubir to present a tutorial at ICASSP 2020

2019/12/06

Michael Fauß, Michael Muma and Abdelhak M. Zoubir to present a tutorial at ICASSP 2020

The tutorial on “Robust Data Science: Modern Tools for Detection, Clustering and Cluster Enumeration” has been accepted for presentation at the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASPP), to be held in Barcelona, Spain between May 4 and May 8, 2020.

Recent advances in the related areas of robust detection and robust cluster analysis for unsupervised learning will be covered. Michael Muma is a Athene Young Investigator of Technische Universität Darmstadt with a research focus on “Robust Statistics for Advanced Signal Processing”. Michael Fauss is a former member of the Signal Processing group and currently with Prof. V. H. Poor at Princeton University. He research work is concerned with sequential analysis and statistical robustness,

The abstract of the tutorial is given below. For further information and registration please visit the ICASSP website.

Abstract

With rapid developments in signal processing and data analytics, driven by technological advances towards a more intelligent networked world, there is an ever-increasing need for reliable and robust information extraction and processing. Robust statistical methods account for the fact that the postulated models for the data are fulfilled only approximately and not exactly. In contrast to classical parametric procedures, robust methods are not significantly affected by small changes in the data, such as outliers or minor model departures. In practice, many engineering applications involve measurements that are not Gaussian and that may contain corrupted measurements and outliers, which cause the data distributions to be heavy-tailed. This leads to a breakdown in performance of traditional signal processing techniques that are based on Gaussian models.

The focus of this tutorial is on recent advances in the related areas of robust detection and robust cluster analysis for unsupervised learning. This tutorial is organized into two parts. In the first part, we discuss robust detection for a given number of hypotheses. In the second part, we move to robust cluster analysis with a focus on recent advances in robust cluster enumeration.

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