DocumentCode :
3119350
Title :
Compressive binary search
Author :
Davenport, Mark A. ; Arias-Castro, Ery
Author_Institution :
Dept. of Stat., Stanford Univ., Stanford, CA, USA
fYear :
2012
fDate :
1-6 July 2012
Firstpage :
1827
Lastpage :
1831
Abstract :
In this paper we consider the problem of locating a nonzero entry in a high-dimensional vector from possibly adaptive linear measurements. We consider a recursive bisection method which we dub the compressive binary search and show that it improves on what any nonadaptive method can achieve. We also establish a non-asymptotic lower bound that applies to all methods, regardless of their computational complexity. Combined, these results show that the compressive binary search is within a double logarithmic factor of the optimal performance.
Keywords :
computational complexity; recursive functions; vectors; adaptive linear measurement; compressive binary search; computational complexity; double logarithmic factor; high-dimensional vector; nonadaptive method; nonasymptotic lower bound; recursive bisection method; Algorithm design and analysis; Context; Matching pursuit algorithms; Noise measurement; Sensors; Testing; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
Conference_Location :
Cambridge, MA
ISSN :
2157-8095
Print_ISBN :
978-1-4673-2580-6
Electronic_ISBN :
2157-8095
Type :
conf
DOI :
10.1109/ISIT.2012.6283595
Filename :
6283595
Link To Document :
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