The following talk will take place at the Computer Science department
this Tuesday (April 9th) at 14:30.
Speaker: Aryeh Kontorovich
Title: Adaptive Metric Dimensionality Reduction
We initiate the study of dimensionality reduction in general metric
spaces in the context of supervised learning. Our statistical
contribution consists of tight Rademacher bounds for Lipschitz
functions in metric spaces that are doubling, or nearly doubling.
As a by-product, we obtain a new theoretical explanation for the
empirically reported improvements gained by pre-processing Euclidean
data by PCA (Principal Components Analysis) prior to constructing a
linear classifier. On the algorithmic front, we describe an analogue
of PCA for metric spaces, namely an efficient procedure that
approximates the data's intrinsic dimension, which is often much
lower than the ambient dimension. Thus, our approach can exploit the dual
benefits of low dimensionality: (1) more efficient proximity search
algorithms, and (2) more optimistic generalization bounds.
Aryeh Kontorovich received his undergraduate degree in
mathematics with a certificate in applied mathematics from
Princeton University in 2001. His M.Sc. and Ph.D. are from
Carnegie Mellon University, where he graduated in 2007. After
a postdoctoral fellowship at the Weizmann Institute of
Science, he joined the Computer Science department at
Ben-Gurion University of the Negev in 2009 as an assistant
professor; this is his current position. His research
interests are mainly in machine learning, with a focus on
probability, statistics, automata theory and metric spaces.
Technion Math. Net (TECHMATH)
Editor: Michael Cwikel <email@example.com>
Announcement from: Orly Avner <firstname.lastname@example.org>