Seminar Details
2011-12-20 (14.00) : A Geometric Approach for Sparse Signal Processing: Algorithms and Performance Guarantees
At Euler building
Organized by Mathematical Engineering
Speaker :
Wei Dai (Imperial College London)
Abstract :
Sparse signal processing is a technology for acquiring and
analyzing sparse signals efficiently. Many signals, including
biomedical signals, sensor network measurements, and social network
data are sparse in the sense that the signal can be well approximated
by only a small number of linear principal components. Sparse signal
processing allows us to work on the intrinsic information directly
without worrying about the details of the signal, hence results in a
large saving in data collection or a huge improvement on data
analysis.
In this talk we discuss a geometric view of sparse signal
processing. More specifically, we look at low-rank matrix completion
and dictionary learning problems in sparse signal processing, show how
these topics can be formulated as optimization problems on Grassmann
manifolds, and discuss what kind of performance guarantees can be
obtained based on the geometric approach. Various applications of
sparse signal processing are also briefly discussed.