On the importance of low-dimensional representations for high-dimensional data

November 9, 11:15 a.m.

2150 Torgersen Hall

People are increasingly inundated with data, in the form of text, imagery, video and audio, with the internet an important delivery mechanism. A challenge concerns making sense of all of this data, and distilling it into useful information. In this talk we discuss the importance of low-dimensional models for representation of such multi-modality high-dimensional data. Low dimensionality is typically imposed in the form of sparsity and via low-rank representations. We discuss such models, methods of inference/computation, and their application to compact/meaningful representations of given high-dimensional data. We also discuss new compressive methods for data acquisition (compressive sensing).

Lawrence Carin/ Duke University

Bio: http://www.ee.duke.edu/faculty/lawrence-carin

Hosts: Layne Watson <ltwatson@computer.org>, Naren Ramakrishnan <naren@cs.vt.edu>
This talk is sponsored by the Discovery Analtytics Center (http://dac.cs.vt.edu)