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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.
Keywords:.
Series
in Communication Theory
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