IC Monday Seminar - An ALPS’ view of Sparse Recovery
Event details
| Date | 22.11.2010 |
| Hour | 16:15 |
| Speaker | Prof. Volkan Cevher, Laboratory for Information and Inference Systems, EPFL, invited by Prof. Willy Zwaenepoel |
| Location |
INM202
|
| Category | Conferences - Seminars |
Abstract : Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just a few (“sparse”) coefficients in a basis. Instead of taking periodic samples, we measure inner products with random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. The standard CS theory dictates that robust recovery is possible from a number of measurements that is commensurate with the sparsity level of signals. The implications are promising for many applications and enable the design of new kinds of analog-to-digital converters, cameras and imaging systems, and sensor networks.
In this talk, we introduce three first-order, iterative CS recovery algorithms, collectively dubbed algebraic pursuits (ALPS), and derive their theoretical convergence and estimation guarantees. We empirically demonstrate that ALPS outperforms the Donoho-Tanner phase transition bounds for sparse recovery using Gaussian, Fourier, and sparse measurement matrices. We then describe how to use ALPS for CS recovery in redundant dictionaries. Finally, we discuss how ALPS can also incorporate union-of-subspaces-based sparsity models in recovery with provable guarantees to make CS better, stronger, and faster.
Bio : Dr. Volkan Cevher received his BSc degree (valedictorian) in Electrical Engineering from Bilkent University in 1999, and his PhD degree in Electrical and Computer Engineering from Georgia Institute of Technology in 2005. He held Research Scientist positions at University of Maryland, College Park during 2006-2007 and at Rice University during 2008-2009. Currently, he is an Assistant Professor at Ecole Polytechnique Federale de Lausanne with joint appointment at the Idiap Research Institute and a Faculty Fellow at Rice University. His research interests include signal processing theory, machine learning and graphical models and information theory.
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Practical information
- General public
- Free
Contact
- Christine Moscioni