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Spectral Methods for Data Clustering
Applied Math| Speaker: | Ming Gu, Dept.of Mathematics, UC Berkeley |
| Location: | 693 Kerr |
| Start time: | Fri, Nov 9 2001, 4:10PM |
Description
In this talk we consider data clustering
using graph models --- the pairwise similarities between all data
objects form a weighted graph adjacency matrix that contains all
necessary information for clustering. We propose new algorithms
for graph partition with an objective function that follows the
min-max clustering principle. We show relationships
between these new algorithms and certain algebraic structures in
the eigenvector and singular vector matrices computed from the
clustering data. We demonstrate the effectiveness
of these methods via data extracted from newsgroup articles.
Brief Bio: Ming Gu received his PhD (1993) degree in Computer Science from Yale
University. He has been with the UCLA math faculty since 1996 and joined the
Berkeley faculty in July 2000. His research interests include
numerical linear algebra, fast algorithm and optimization.
Coffee/cookies @ 693 Kerr