[Colloquium] Zhou/MS Presentation/Nov. 6, 2008

[Margaret Jaffey]

This is an announcement of Xueyuan Zhou's MS Presentation.

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Date:  Thursday, November 6, 2008

Time:  12:00 noon

Place:  Ryerson 277

M.S. Candidate:  Xueyuan Zhou

M.S. Paper Title:  Exploiting Geometric Structure of High Dimensional  
Data for Learning: An Empirical Study

Abstract:
In machine learning, high dimensional data generally should have a
high degree of freedom. However, recent experiments in machine learn-
ing show that real world data in high dimensions is usually governed
by a surprisingly low dimensions. We believe that in high dimensions,
geometry information, for example, the “shape” of data distribution,
can help learning algorithms to perform better. A geometric trans-
form of high dimensions targeted for learning is attractive for high
dimensional machine learning problems.
In this paper, we gave an empirical evaluation by experiments com-
paring how geometric information of high dimensional data can help
for learning. We consider two geometric transforms, Laplacian Eigen-
maps and Diffusion maps as our general geometric transforms. Dis-
tance in spaces after the transform is discussed. We compared clas-
sification results from data in original spaces to a new representation
of the data after geometric transforms in various application areas,
including image, text, acoustic signals, microarray data, and  
artificial
data sets. Results showed that learning algorithms can take advan-
tage of geometric information for most real world data in high dimen-
sions. When labeled examples are extremely few, geometric trans-
forms showed great improvement in learning. We also found cases
when these geometric transforms fail in artificial data sets. General
conditions when the transforms can result in better classifications are
discussed.

Advisor:  Prof. Partha Niyogi

A draft copy of Mr. Zhou's MS Paper is available in Ry 156.


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Margaret P. Jaffey                             margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 156)        (773) 702-6011
The University of Chicago                  http://www.cs.uchicago.edu
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