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Sparse Representation in Redundant Systems
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Emmanuel Candes
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CSIC Building (#406),
Seminar Room 4122.
Directions: home.cscamm.umd.edu/directions
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Decoding by Linear Programming and Other Perhaps Surprising Phenomena
Dr. Emmanuel Candes
Applied / Computational Mathematics at California Institute of Technology
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Abstract:
Suppose we wish to transmit an n-dimensional vector
f (the ``plaintext'). We generate another
m-dimensional vector Af (the ``ciphertext') where A
is an m by n matrix with m > n which is to be sent.
A recurrent problem with real communication or
storage devices is that some of the entries of the
ciphertext may become corrupted; the corrupted
entries are unreliable and may have nothing to do
with the original values. We do not know which
entries are affected nor do we know how they are
affected. We will show that no matter what the
corruption is like, it is provably possible to
recover the original message by solving a convenient
linear program (provided that the fraction of
corrupted entries is not excessively large). This
work is related to the problem of finding sparse
solutions to vastly underdetermined systems of
linear equations. I will discuss these significant
connections and introduce a new collection of
results showing that it is possible to recover
sparse or compressible signals accurately, and
sometimes even exactly, from highly incomplete
measurements. Parts of this work are joint with
Terence Tao and Justin Romberg.
[LECTURE SLIDES]
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