By Topic

Nuclear detection using Higher-Order topic modeling

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)
Christie Nelson ; RUTCOR, Rutgers University, Piscataway, New Jersey, USA ; William M. Pottenger ; Hannah Keiler ; Nir Grinberg

In this paper, a novel approach to topic modeling based on the Higher Order Learning framework, Higher-Order Latent Dirichlet Allocation (HO-LDA), is applied to a critical issue in homeland security, nuclear detection. In addition, this research strives to improve topic models in the `real time' environment of online learning. In total, seventeen different nuclear radioisotopes are classified, and performance of Higher-Order versus traditional techniques is evaluated. This project employs LDA and HO-LDA on a nuclear detection numeric dataset to gain a topic decomposition of instances. These learned topics are then used as features in a traditional supervised classification algorithm. In essence, the LDA or HO-LDA topic assignments are used as features in supervised learning algorithms that predict the class (isotope), treating LDA or HO-LDA as a feature space transform. Using Topic Modeling on numeric nuclear detection data is cutting edge, as to our knowledge this has never been done before on a nuclear detection dataset. Two methods of feature transformation are evaluated, including Multinomial Feature Creation and Maximum Channel Value Feature Creation. Results demonstrate further evidence that Higher Order Learning techniques can be usefully applied in topic modeling applied to nuclear detection.

Published in:

Homeland Security (HST), 2012 IEEE Conference on Technologies for

Date of Conference:

13-15 Nov. 2012