Feature selection using sparse Bayesian inference

T. Scott Brandes; James R. Baxter; Jonathan Woodworth

doi:10.1117/12.2058255

13 June 2014 Feature selection using sparse Bayesian inference

T. Scott Brandes, James R. Baxter, Jonathan Woodworth

Proceedings Volume 9093, Algorithms for Synthetic Aperture Radar Imagery XXI; 90930E (2014) https://doi.org/10.1117/12.2058255
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

T. Scott Brandes, James R. Baxter, and Jonathan Woodworth "Feature selection using sparse Bayesian inference ", Proc. SPIE 9093, Algorithms for Synthetic Aperture Radar Imagery XXI, 90930E (13 June 2014); https://doi.org/10.1117/12.2058255

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