Algorithmic Issues in Sparse Approximation Theory

ATTENTION: THIS WEB SITE HAS MOVED. The pages you are looking at are no longer being maintained. Please go to http://www.poly.edu/cis/ to visit the new site of the Department of Computer and Information Science at Polytechnic University.
Algorithmic Issues in Sparse Approximation Theory

S. Muthukrishnan
Rutgers University

Friday, Sep. 30, 11:00am
LC 102, Brooklyn Campus, Polytechnic University

Abstract
        We are given a dictionary D of N dimensional vectors. Later, we are given a signal A that is also an N dimensional vector. Our problem is to find the best B term representation - linear combination of B vectors from D - for the given signal A. This is the general problem in sparse approximation theory and it has applications in many areas, such as signal processing, harmonic analysis, communication theory, and compression, depending on the nature of D and the notion of error in the representation. In this talk, I will provide an overview of algorithmic results on this problem, with special focus on "Compressed Sensing", a new direction formulated recently.

Bio

      S. Muthu Muthukrishnan is an Associate Professor in the Computer Science Department at Rutgers University. His research interests are in algorithms, databases, and networking. More information con be obtained from his homepage at http://www.cs.rutgers.edu/~muthu.

      For further information please contact Torsten Suel (suel at poly.edu)