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Proceedings Paper

Feature selection using sparse Bayesian inference
Author(s): T. Scott Brandes; James R. Baxter; Jonathan Woodworth
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Paper Abstract

A process for selecting a sparse subset of features that maximize discrimination between target classes is described in a Bayesian framework. Demonstrated on high range resolution radar (HRR) signature data, this has the effect of selecting the most informative range bins for a classification task. The sparse Bayesian classifier (SBC) model is directly compared against Fisher's linear discriminant analysis (LDA), showing a clear performance gain with the Bayesian framework using HRRs from the publicly available MSTAR data set. The discriminative power of the selected features from the SBC is shown to be particularly dominant over LDA when only a few features are selected or when there is a shift in training and testing data sets, as demonstrated by training on a specific target type and testing on a slightly different target type.

Paper Details

Date Published: 13 June 2014
PDF: 7 pages
Proc. SPIE 9093, Algorithms for Synthetic Aperture Radar Imagery XXI, 90930E (13 June 2014); doi: 10.1117/12.2058255
Show Author Affiliations
T. Scott Brandes, Signal Innovations Group, Inc. (United States)
James R. Baxter, Signal Innovations Group, Inc. (United States)
Jonathan Woodworth, Signal Innovations Group, Inc. (United States)

Published in SPIE Proceedings Vol. 9093:
Algorithms for Synthetic Aperture Radar Imagery XXI
Edmund Zelnio; Frederick D. Garber, Editor(s)

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