PPT-Online Performance Guarantees for Sparse Recovery

Raja Giryes ICASSP 2011 Volkan Cevher Agenda The sparse approximation problem Algorithms and prerun guarantees Online performance guarantees Performance bound Parameter selection 2 Sparse approximation. Such matrices has several attractive properties they support algorithms with low computational complexity and make it easy to perform in cremental updates to signals We discuss applications to several areas including compressive sensing data stream Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. . http://lions.epfl.ch. Linear Dimensionality Reduction. Compressive sensing. non-adaptive measurements. Compressive Sensing of Videos. Venue. CVPR 2012, Providence, RI, USA. June 16, 2012. Organizers. :. Richard G. . Baraniuk. Mohit. Gupta. Aswin C. Sankaranarayanan. Ashok Veeraraghavan. Part 2: Compressive sensing. 26. th. . August 2013. Abdul Yassim. Vice President. Trade and Working Capital. 1. Absa presentation. title .  Date of presentation. Company confidential use only / Unrestricted distribution. . to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. Aditya. Chopra and Prof. Brian L. Evans. Department of Electrical and Computer Engineering. The University of Texas at Austin. 1. Introduction. Finite Impulse Response (FIR) model of transmission media. Recovery. . (. Using . Sparse. . Matrices). Piotr. . Indyk. MIT. Heavy Hitters. Also called frequent elements and elephants. Define. HH. p. φ. . (. x. ) = { . i. : |x. i. | ≥ . φ. ||. x||. p. Adaptivity. in Sparse Recovery. Piotr. . Indyk. MIT. Joint work . with Eric . Price and David Woodruff, 2011.. Sparse recovery. (approximation theory, statistical model selection, information-based complexity, learning Fourier . Ron Rubinstein. Advisor: Prof. Michael . Elad. October 2010. Signal Models. Signal models. . are a fundamental tool for solving low-level signal processing tasks. Noise Removal. Image Scaling. Compression. Sparse Matrices. Morteza. . Mardani. , Gonzalo . Mateos. and . Georgios. . Giannakis. ECE Department, University of Minnesota. Acknowledgments. : . MURI (AFOSR FA9550-10-1-0567) grant. Ann Arbor, USA. Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. . Jeremy Watt and . Aggelos. . Katsaggelos. Northwestern University. Department of EECS. Part 2: Quick and dirty optimization techniques. Big picture – a story of 2’s. 2 excellent greedy algorithms: . Dileep Mardham. Introduction. Sparse Direct Solvers is a fundamental tool in scientific computing. Sparse factorization can be a challenge to accelerate using GPUs. GPUs(Graphics Processing Units) can be quite good for accelerating sparse direct solvers.