DocumentCode :
2803088
Title :
Empirical quantization for sparse sampling systems
Author :
Lexa, Michael A.
Author_Institution :
Inst. for Digital Commun., Univ. of Edinburgh, Edinburgh, UK
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
3942
Lastpage :
3945
Abstract :
We propose a quantization design technique (estimator) suitable for new compressed sensing sampling systems whose ultimate goal is classification or detection. The design is based on empirical divergence maximization, an approach akin to the well-known technique of empirical risk minimization. We show that the estimator´s rate of convergence to the “best in class” estimate can be as fast as n-1, where n equals the number of training samples.
Keywords :
quantisation (signal); compressed sensing sampling systems; empirical divergence maximization; empirical quantization; quantization design technique; sparse sampling systems; Analog-digital conversion; Compressed sensing; Convergence; Demodulation; Digital communication; Quantization; Risk management; Sampling methods; Signal detection; Signal sampling; Kullback-Leibler divergence; compressed sensing; empirical estimators; quantization for classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495786
Filename :
5495786
Link To Document :
بازگشت