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Series in Communication Theory 

Edited by Helmut Bölcskei

Vol. 12:



Reinhard W. Heckel,

Sparse Signal Processing: Subspace Clustering and System Identification

1st edition 2014. XXII, 182 pages; € 64,00. ISBN 978-3-86628-513-2


An important problem in statistics, machine learning, and modern signal processing is to recover information of limited complexity, or, more specifically, of low-dimensional structure, from seemingly few data.

Often, this amounts to recover a sparse signal, i.e., a signal which is non-zero at few locations only, by solving an under-determined system of linear equations. In this thesis, we will use ideas from sparse signal recovery to cluster high dimensional data points, to identify sparse linear operators, and to recover sparse signals with certain block-structure.



About the author:


Reinhard Heckel was born in Biberach, Germany, in 1986. He received the Dipl.-Ing. degree (with distinction) in electrical engineering in 2010 from the University of Ulm, Germany. In 2010, he joined the Communication Technology Laboratory at ETH Zurich, where he graduated with the Dr. Sc. degree in 2014. From September to December 2013, he was a visiting researcher at Stanford University, Stanford, CA.





Series in Communication Theory

Hartung-Gorre Verlag Konstanz

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