By Topic

Unsupervised learning of finite mixture models using mean field games

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

4 Author(s)
Sergio Pequito ; Department of Electrical and Computer Engineering, Institute for System and Robotics, Instituto Superior Tecnico, Technical University of Lisbon, Lisbon, Portugal ; A. Pedro Aguiar ; Bruno Sinopoli ; Diogo A. Gomes

In this paper we develop a dynamic continuous solution to the clustering problem of data characterized by a mixture of K distributions, where K is given a priori. The proposed solution resorts to game theory tools, in particular mean field games and can be interpreted as the continuous version of a generalized Expectation-Maximization (GEM) algorithm. The main contributions of this paper are twofold: first, we prove that the proposed solution is a GEM algorithm; second, we derive closed-form solution for a Gaussian mixture model and show that the proposed algorithm converges exponentially fast to a maximum of the log-likelihood function, improving significantly over the state of the art. We conclude the paper by presenting simulation results for the Gaussian case that indicate better performance of the proposed algorithm in term of speed of convergence and with respect to the overlap problem.

Published in:

Communication, Control, and Computing (Allerton), 2011 49th Annual Allerton Conference on

Date of Conference:

28-30 Sept. 2011