Paper Info
Title
Data Separation by Sparse Representations
Abstract
Recently, sparsity has become a key concept in various areas of applied mathematics, computer science, and electrical engineering. One application of this novel methodology is the separation of data, which is composed of two (or more) morphologically distinct constituents. The key idea is to carefully select representation systems each providing sparse approximations of one of the components. Then the sparsest coefficient vector representing the data within the composed - and therefore highly redundant - representation system is computed by $\ell_1$ minimization or thresholding. This automatically enforces separation. This paper shall serve as an introduction to and a survey about this exciting area of research as well as a reference for the state-of-the-art of this research field. It will appear as a chapter in a book on "Compressed Sensing: Theory and Applications" edited by Yonina Eldar and Gitta Kutyniok.
Year
DOI
Venue
2011
10.1017/CBO9780511794308.012
Clinical Orthopaedics and Related Research
Keywords
Field
DocType
coherence. `1 minimization. morphology. separation. sparse represen- tation. tight frames.,electrical engineering,sparse representation,sparse approximation,compressed sensing
Data separation,Mathematical analysis,Approximations of π,Algorithm,Theoretical computer science,Minification,Thresholding,Mathematics,Compressed sensing
Journal
Volume
ISSN
Citations 
abs/1102.4
in: "Compressed Sensing: Theory and Applications", Cambridge University Press, 2011
5
PageRank 
References 
Authors
0.55
15
1
Name
Order
Citations
PageRank
Gitta Kutyniok132534.77