By Topic

Probabilistic clustering based on Langevin mixture

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

2 Author(s)
Ola Amayri ; Electrical and Computer Engineering Department Concordia University Montreal, Canada, H3G 2W1 ; Nizar Bouguila

In this paper, we propose a statistical framework for clustering spherical data which are usually found in machine learning, data mining and computer vision applications. Our framework is based on finite Langevin mixture models which provide a very natural representation of normalized vectors in high dimensional spaces in which the data lie on unit hypersphere. Moreover, we developed minimum message length (MML) criterion for the selection of finite Langevin mixture components from which different probabilistic information divergence distances are then derived. Through empirical experiments, we demonstrate the merits of the proposed learning framework through challenging applications involving spam filtering using visual email content and email categorization.

Published in:

Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on  (Volume:2 )

Date of Conference:

18-21 Dec. 2011