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.
Cite this article
Ingrid Daubechies, Gitta Kutyniok, Holger Rauhut, Thomas Strohmer, Applied Harmonic Analysis and Data Processing. Oberwolfach Rep. 15 (2018), no. 1, pp. 723–792DOI 10.4171/OWR/2018/14