By Topic

Kalman filtering in stochastic gradient algorithms: construction of a stopping rule

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
$31 $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

2 Author(s)
Bittner, B. ; CNRS, Univ. de Nice-Sophia Antipolis, Sophia Antipolis, France ; Pronzato, L.

Stochastic gradient algorithms are widely used in signal processing. Whereas stopping rules for deterministic descent algorithms can easily be constructed, using for instance the norm of the gradient of the objective function, the situation is more complicated for stochastic methods since the gradient needs first to be estimated. We show how a simple Kalman filter can be used to estimate the gradient, with some associated confidence, and thus construct a stopping rule for the algorithm. The construction is illustrated by a simple example. The filter might also be used to estimate the Hessian, which would open the way to a possible acceleration of the algorithm. Such developments are briefly discussed.

Published in:

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on  (Volume:2 )

Date of Conference:

17-21 May 2004