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

Author : danika-pritchard | Published Date : 2018-03-17

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

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Large-Scale Distributed Non-negative Spa..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Large-Scale Distributed Non-negative Sparse Coding and Sparse Dictionary Learning: Transcript

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 . By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . IT 530, Lecture Notes. Introduction: Complete and over-complete bases. Signals are often represented as a linear combination of basis functions (e.g. Fourier or wavelet representation).. The basis functions always have the same dimensionality as the (discrete) signals they represent.. 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 . nauseous AVqeMtsp like http://www.merriam-webster.com/dictionary/nauseous AVqeMtsp http://www.merriam-webster.com/dictionary/nauseous like http://www.merriam-webster.com/dictionary/nauseous captured 3 Sparsity. and Geometry Constrained Dictionary Learning for Action. Recognition from Depth Maps. Jiajia. . Luo. , Wei Wang, and . Hairong. Qi. The University of Tennessee, Knoxville. Presented by: Marwan . 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. Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). 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 . (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. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. Conclusions. Outline. Unusual Event Detection. Video Representation. Dynamic Sparse Coding. Empirical Study. Draw your own picture dictionary. The contents of your dictionary can be found on this page. For each term included you will need to fill in either a missing illustration, a missing definition or a missing entry term. Use texts and laptops/. Reading Group Presenter:. Zhen . Hu. Cognitive Radio Institute. Friday, October 08, 2010. Authors: Carlos M. . Carvalho. , Nicholas G. Polson and James G. Scott. Outline. Introduction. Robust Shrinkage of Sparse Signals. 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 .

Download Document

Here is the link to download the presentation.
"Large-Scale Distributed Non-negative Sparse Coding and Sparse Dictionary Learning"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.