Sparse dimensionality reduction: beyond worst-case analysis
, Harvard University
Date: Tuesday, February 25, 2014
Time: 4:15 PM to 5:15 PM Note: all times are in the Eastern Time Zone
Refreshments: 3:45 PM
Host: Ankur Moitra
Contact: Holly A Jones, firstname.lastname@example.org
Relevant URL: http://toc.csail.mit.edu/node/491
Speaker URL: None
email@example.com, firstname.lastname@example.org, email@example.com
TALK: Sparse dimensionality reduction: beyond worst-case analysis
This talk will discuss sparse Johnson-Lindenstrauss
transforms, i.e. sparse linear maps into much lower dimension which
preserve the Euclidean geometry of a set of vectors. We derive upper
bounds on the sufficient target dimension and sparsity of the
projection matrix to achieve good dimensionality reduction. Our bounds
depend on the geometry of the set of vectors, moving us away from
worst-case analysis and toward instance-optimality.
Joint work with Jean Bourgain.
Created by Holly A Jones at Wednesday, February 19, 2014 at 10:42 AM.