PPT-Sparse

Modeling of GraphStructured Data and Images 1 Michael Elad The Computer Science Department The Technion T he research leading to these results . 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 Aswin C Sankaranarayanan. Rice University. Richard G. . Baraniuk. Andrew E. Waters. Background subtraction in surveillance videos. s. tatic camera with foreground objects. r. ank 1 . background. s. parse. From Theory to Practice . Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . Raja . Giryes. ICASSP 2011. Volkan. Cevher. Agenda. The sparse approximation problem. Algorithms and pre-run guarantees. Online performance guarantees. Performance bound. Parameter selection. 2. Sparse approximation. 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. Full storage:. . 2-dimensional array.. (nrows*ncols) memory.. 31. 0. 53. 0. 59. 0. 41. 26. 0. 31. 41. 59. 26. 53. 1. 3. 2. 3. 1. Sparse storage:. . Compressed storage by columns . (CSC).. Three 1-dimensional arrays.. 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. Tianzhu . Zhang. 1,2. , . Adel Bibi. 1. , . Bernard Ghanem. 1. 1. 2. Circulant. Primal . Formulation. 3. Dual Formulation. Fourier Domain. Time . Domain. Here, the inverse Fourier transform is for each . 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. Molinaro. Santanu. . Dey. , Andres . Iroume. , . Qianyi. Wang. Georgia Tech. Better . approximation. of the integer hull. CuttinG. -planes. IN THEORY. Can use . any . cutting-plane. Putting all gives . Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . Yi Ma. 1,2. . Allen Yang. 3. John . Wright. 1. CVPR Tutorial, June 20, 2009. 1. Microsoft Research Asia. 3. University of California Berkeley. 2. University of Illinois . at Urbana-Champaign. Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . Hassanieh. , P. . Indyk. Parallelization of Sparse Coding & Dictionary Learning Univeristy of Colorado Denver Parallel Distributed System Fall 2016 Huynh Manh 11/15/2016 1 Contents Introduction to Sparse Coding Applications of Sparse Representation