By Topic

Nonparametric decentralized detection using kernel methods

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
X. Nguyen ; Dept. of Electr. Eng. & Comput. Sci., Univ. of California, Berkeley, CA, USA ; M. J. Wainwright ; M. I. Jordan

We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. In contrast to most previous work, in which the joint distribution of sensor observations is assumed to be known, we address the problem when only a set of empirical samples is available. We propose a novel algorithm using the framework of empirical risk minimization and marginalized kernels and analyze its computational and statistical properties both theoretically and empirically. We provide an efficient implementation of the algorithm and demonstrate its performance on both simulated and real data sets.

Published in:

IEEE Transactions on Signal Processing  (Volume:53 ,  Issue: 11 )