Oberwolfach Reports


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Volume 15, Issue 1, 2018, pp. 723–792
DOI: 10.4171/OWR/2018/14

Published online: 2019-01-04

Applied Harmonic Analysis and Data Processing

Ingrid Daubechies[1], Gitta Kutyniok[2], Holger Rauhut[3] and Thomas Strohmer[4]

(1) Duke University, Durham, USA
(2) Technische Universität Berlin, Germany
(3) Rheinisch-Westfälische Technische Hochschule Aachen, Germany
(4) University of California at Davis, USA

Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, or classification. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Two previous very successful workshops on applied harmonic analysis and sparse approximation have taken place in 2012 and in 2015. This workshop was the an evolution and continuation of these workshops and intended to bring together world leading experts in applied harmonic analysis, data analysis, optimization, statistics, and machine learning to report on recent developments, and to foster new developments and collaborations.

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Daubechies Ingrid, Kutyniok Gitta, Rauhut Holger, Strohmer Thomas: Applied Harmonic Analysis and Data Processing. Oberwolfach Rep. 15 (2018), 723-792. doi: 10.4171/OWR/2018/14