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Series in Communication Theory
Edited by Helmut Bölcskei
Vol. 11:
Graeme Pope,
Structured Sparce Signal Recovery in General
Hilbert Spaces
1st edition 2014. XXII, 196 pages; € 64,00. ISBN 3-86628-514-0
978-3-86628-514-9
Data analysis is fundamental to many parts of modern
society. This work examines how we can use sparsity to extract meaning and
structure from complex data sets.
We begin by establishing a general framework for
imposing a model on the sparse representation of a signal. This not only encompasses many of the
existing theoretical results, but allows us to use more complex notions of
sparsity in our models. We present both
deterministic recovery thresholds and probabilistic results stating when we can
recover sparse signals and what we gain by using these more complex notions of
sparsity.
Furthermore, we develop a new notion of sparsity which
we use to identify peptides in biological samples. We show how we use our new framework to
model these particular signals and then, due to the overwhelming problem size,
we develop specific algorithms to solve this problem in an efficient manner.
About the
author
Graeme Pope was born in Canberra, Australia, in 1984.
He received a BSc with First Class Honours
and University Medal in Pure Mathematics in 2006 and a BEng with First Class Honours and University Medal in Electrical Engineering in
2007 from the University of Sydney, Australia.
In 2009 he received a MSc in
Computer Science from ETH Zurich, Switzerland, after which he joined the
Communication Technology Laboratory at ETH Zurich, where he graduated with the
Dr. sc. degree in Electrical Engineering in 2013.
Keywords:
Uncertainty relations, Sparse signal recovery, Sparse noise, Deterministic
recovery guarantees, Probabilistic recovery guarantees,l1
minimization, Coherence, Basis Pursuit, Compressed sensing, Nuclear Norm
Minimization, Block Sparsity
Series in Communication
Theory
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