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Proceedings Paper

Classification by using Prony's method with a polynomial model
Author(s): R. Mueller; W. Lee; J. Okamitsu
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Paper Abstract

Prony's Method with a Polynomial Model (PMPM) is a novel way of doing classification. Given a number of training samples with features and labels, it assumes a Gaussian mixture model for each feature, and uses Prony's method to determine a method of moments solution for the means and priors of the Gaussian distributions in the Gaussian mixture model. The features are then sorted in descending order by their relative performance. Based on the Gaussian mixture model of the first feature, training samples are partitioned into clusters by determining which Gaussian distribution each training sample is most likely from. Then with the training samples in each cluster, a new Gaussian mixture model is built for the next most powerful feature. This process repeats until a Gaussian mixture model is built for each feature, and a tree is thus grown with the training data partitioned into several final clusters. A "leaf" model for each final cluster is the weighted least squares solution (regression) for approximating a polynomial function of the features to the truth labels. Testing consists of determining for each testing sample a likelihood that the testing sample belongs to each cluster, and then regressions are weighted by their likelihoods and averaged to produce the test confidence. Evaluation of PMPM is done by extracting features from data collected by both Ground Penetrating Radar and Metal Detector of a robot-mounted land-mine detection system, training PMPM models, and testing in a cross-validation fashion.

Paper Details

Date Published: 18 May 2012
PDF: 8 pages
Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 835726 (18 May 2012); doi: 10.1117/12.923524
Show Author Affiliations
R. Mueller, NIITEK Inc. (United States)
W. Lee, NIITEK Inc. (United States)
J. Okamitsu, NIITEK Inc. (United States)

Published in SPIE Proceedings Vol. 8357:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII
J. Thomas Broach; John H. Holloway Jr., Editor(s)

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