PPT-Large-Scale Distributed Non-negative Sparse Coding and Sparse Dictionary Learning

Author Vikas Sindhwani and Amol Ghoting Presenter Jinze Li Problem Introduction we are given a collection of N data points or signals in a highdimensional space R D xi . 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 illinoisedu kyusvneclabscom Abstract Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data Empirical studies show that mapping the data into a signi64257cantly higher di Origin, Definition, Pursuit, Dictionary-Learning and Beyond. Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. . Mathematics & Image Analysis (MIA) 2012 Workshop – Paris . Analysis . Sparse Models. Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. . SPARS11 Workshop:. . . Signal . Processing with Adaptive . Ph.D. Thesis Defense. Anoop Cherian. *. Department of Computer Science and Engineering. University of Minnesota, Twin-Cities. Adviser. : Prof. Nikolaos Papanikolopoulos. *Contact: . cherian@cs.umn.edu. 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. 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. (Paper ID: 2314). Vishwanath Saragadam,. . Aswin. . Sankaranarayanan. ,. Xin Li. 1. Compressive sensing. Solving underdetermined linear system of equations. Relies on sparsity of signal. Orthogonal Matching Pursuit. Optimization. Micha . Feigin. , . Danny Feldman. , . Nir. . Sochen. Coresets. Mean Queries. Mean Queries. Mean Queries. Definition. Coresets for Mean Queries. Coresets for Mean Queries. Coresets for Mean Queries. KH Wong. mean transform v.5a. 1. Introduction. What is object tracking. Track an object in a video, the user gives an initial bounding box. Find the bounding box that cover the target pattern in every frame of the video. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. Conclusions. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. 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. Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . 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