Interesting Lecture on Wednesday, Feb 28, 2007 - "Minimum Noiseless Description Length (MNDL)"
Bruno Di Stefano
Toronto, Ontario, Canada
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Title
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Minimum Noiseless Description Length (MNDL)
Speaker
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Soosan Beheshti, Ph.D.
Assistant Professor
ELCE Department
Ryerson University
Day and Time
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Wednesday, February 28, 2007, 6:00 p.m. - 7:30 p.m.
6:00 Arrival & Networking
6:30 Talk
7:30 Conclusion
Note
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the WIE Affinity Group has scheduled a social networking event to
follow this seminar.
See: http://toronto.ieee.ca/events/feb28207.htm
Location
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Room BA 1240
Bahen Centre for Information Technology
University of Toronto - St. George Campus
40 St. George Street
Map (use code BA) ==> http://oracle.osm.utoronto.ca/map/index2.html
Organizer
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IEEE Toronto Signals & Computational Intelligence Joint Chapter
Contact
======
Bruno Di Stefano, P.Eng., b DOT distefano AT ieee DOT org
http://bruno.distefano.googlepages.com/home
Abstract
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The purpose of statistical modeling is to discover regularities and
structure in observed data. As any finite length data set can be
represented by a string of symbols from a finite alphabet, any
regularity in a given data set can be used to compress the data. In
this process, Occam's razor is interpreted as counseling the use of
simpler models rather than complex ones and fewer symbols rather than
more symbols. A well known approach to this modeling problem is
Minimum Description Length (MDL). I have recently developed a new
approach to the statistical modeling of noisy data denoted by Minimum
Noiseless Description Length (MNDL). The main difference between these
two approaches is that the conventional MDL compares the description
length of the ``noisy" data, while the MNDL compares the description
length of the desired "noiseless" data.
In this presentation, we review the basics of MDL approach and present
the fundamentals of MNDL statistical modeling. The application of MNDL
in best basis selection and compression will be presented. We will
compare MNDL thresholding with existing thresholding methods with an
example in wavelet image denoising and demonstrate its effective
performance for frequency resolution improvement in nonparametric
power spectral density (PSD) estimation. We present the advantages and
drawbacks of MNDL and discuss its potential for applications in
various areas.
Biography
========
Soosan Beheshti received the B.S. degree from Isfahan Institute of
Technology, and the M.S. and Ph.D. degrees from Massachusetts
Institute of Technology (MIT) in 1996 and 2002, respectively, all in
electrical engineering. During her graduate studies, she was member of
Digital Signal Processing Group and Laboratory for Information and
Decision Systems and received the MIT EECS Carlton E. Tucker Award for
Teaching Excellence. From 2002 to 2005, she was postdoctoral associate
and lecturer at MIT. She has been with the ELCE Department of Ryerson
University as an Assistant Professor since July 2005. Her research
interests include information processing and statistical learning
theory.
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Feel free to bring along any colleague who may be interested in this
talk.
The "Signals & Computational Intelligence Joint Chapter" is an IEEE
Toronto chapter of:
- IEEE Aerospace and Electronic Systems Society
- IEEE Computational Intelligence Society
- IEEE Geoscience and Remote Sensing Society
- IEEE Oceanic Engineering Society
- IEEE Ultrasound, Ferroelectrics, and Frequency Control Society
- IEEE Vehicular Technology Society
If you are a member of one or more of these societies and wish to be
active in our chapter, please, write to me an short e-mail message.
Thank you.
Best regards
Bruno Di Stefano
PS
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Here are relevant web pages:
- http://toronto.ieee.ca/
- http://toronto.ieee.ca/chapters/s_ci.htm
- http://toronto.ieee.ca/events/feb2807.htm
- http://toronto.ieee.ca/executive/distefano.htm
--
-- Bruno Di Stefano
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http://bruno.distefano.googlepages.com/home
http://www3.sympatico.ca/nuptek
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