NDLI: Empirical divergence maximization for quantizer design: An analysis of approximation error

Content Provider	IEEE Xplore Digital Library
Author	Lexa, M.A.
Copyright Year	2011
Description	Author affiliation: Institute for Digital Communications, The University of Edinburgh, UK (Lexa, M.A.)
Abstract	Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions that induce probability distributions. We use this method as a design strategy for quantizers whose output will ultimately be used to make a decision about the quantizer's input. We derive this estimator's approximation error decay rate as a function of the resolution of a class of partitions known as recursive dyadic partitions. This result, coupled with earlier results, show that this estimator can converge to the theoretically optimal solution as fast as $n^{−1},$ where n is the number of training samples. This estimator also is capable of producing estimates that well-approximate optimal solutions that existing techniques cannot.
Starting Page	4220
Ending Page	4223
File Size	645693
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5947284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantization Approximation error Convergence Estimation error Level set recursive dyadic partitions empirical quantizer design empirical divergence maximization Kullback-Leibler divergence
Content Type	Text
Resource Type	Article

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