Campuses:

Change-point Detection for High-Dimensional Timeseries<br/><br/>with Missing Data

Tuesday, September 24, 2013 - 9:00am - 9:50am
Lind 305
Rebecca Willett Lu (University of Wisconsin, Madison)
In this talk, I will describe a novel approach to change-point
detection when the observed high-dimensional data may have missing
elements. The performance of classical methods for change-point
detection typically scales poorly with the dimensionality of the data,
so that a large number of observations are collected after the true
change-point before it can be reliably detected. Furthermore, missing
components in the observed data handicap conventional approaches. The
proposed method addresses these challenges by modeling the dynamic
distribution underlying the data as lying close to a time-varying
low-dimensional submanifold embedded within the ambient observation
space. Specifically, streaming data is used to track a submanifold
approximation, measure deviations from this approximation, and
calculate a series of statistics of the deviations for detecting when
the underlying manifold has changed in a sharp or unexpected manner.
The proposed approach leverages several recent results in the field of
high-dimensional data analysis, including subspace tracking with
missing data, multiscale analysis techniques for point clouds, online
optimization, and change-point detection performance analysis.
Simulations and experiments highlight the robustness and efficacy of
the proposed approach in detecting an abrupt change in an otherwise
slowly varying low-dimensional manifold.

Rebecca Willett is an associate professor in the Electrical and
Computer Engineering Department at the University of
Wisconsin-Madison. She completed her PhD in Electrical and Computer
Engineering at Rice University in 2005 and was an assistant then
associate professor of Electrical and Computer Engineering at Duke
University from 2005 to 2013. Prof. Willett received the National
Science Foundation CAREER Award in 2007, is a member of the DARPA
Computer Science Study Group, and received an Air Force Office of
Scientific Research Young Investigator Program award in 2010. Prof.
Willett has also held visiting researcher positions at the Institute
for Pure and Applied Mathematics at UCLA in 2004, the University of
Wisconsin-Madison 2003-2005, the French National Institute for
Research in Computer Science and Control (INRIA) in 2003, and the
Applied Science Research and Development Laboratory at GE Healthcare
in 2002. Her research interests include network and imaging science
with applications in medical imaging, neural coding, astronomy, and
social networks.
MSC Code: 
93E10
Keywords: