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<td>[math-ias] Reminder for today's Members' Seminar</td>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">Date: </th>
<td>Mon, 28 Oct 2019 11:00:03 -0400</td>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">From: </th>
<td>Anthony Pulido <a class="moz-txt-link-rfc2396E" href="mailto:apulido@ias.edu"><apulido@ias.edu></a></td>
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<td>Seminars <a class="moz-txt-link-rfc2396E" href="mailto:seminars@math.ias.edu"><seminars@math.ias.edu></a></td>
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<br>
INSTITUTE FOR ADVANCED STUDY<br>
School of Mathematics<br>
Princeton, NJ 08540<br>
<br>
Members' Seminar<br>
Monday, October 28<br>
<br>
<br>
Topic: Sparse matrices in sparse analysis<br>
Speaker: Anna Gilbert, University of Michigan; Member, School
of Mathematics<br>
Time/Room: 2:00pm - 3:00pm/Simonyi Hall 101<br>
Abstract Link:
<a class="moz-txt-link-freetext" href="http://www.math.ias.edu/seminars/abstract?event=129461">http://www.math.ias.edu/seminars/abstract?event=129461</a><br>
<br>
In this talk, I will give two vignettes on the theme of sparse
matrices in sparse analysis. The first vignette covers work from
compressive sensing in which we want to design sparse matrices
(i.e., matrices with few non-zero entries) that we use to
(linearly) sense or measure compressible signals. We also design
algorithms such that, from these measurements and these matrices,
we can efficiently recover a compressed, or sparse, representation
of the sensed data. I will discuss the role of expander graphs and
error correcting codes in these designs and applications to high
throughput biological screens. The second vignette flips the
theme; suppose we are given a distance or similarity matrix for a
data set that is corrupted in some fashion, find a sparse
correction or repair to the distance matrix so as to ensure the
corrected distances come from a metric; i.e., repair as few
entries as possible in the matrix so that we have a metric. I will
discuss generalizations to graph metrics, applications to (and
from) metric embeddings, and algorithms for variations of this
problem. I will also touch upon applications in machine learning
and bio-informatics.<br>
<br>
<a class="moz-txt-link-freetext" href="http://www.math.ias.edu/seminars">http://www.math.ias.edu/seminars</a><br>
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