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Proceedings Paper

Class-specific feature selection based on uniform dirichlet priors
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Paper Abstract

In this paper, the Bayesian Data Reduction Algorithm (BDRA) is applied to reducing the dimensionality of a data set that contains class-specific feature. The BDRA uses the probability of error, conditioned on the training data, and a 'greedy' approach for reducing irrelevant features from the data. Here, the BDRA is shown to be an effective means of selecting binary valued class-specific feature, where the remaining non-class-specific features are irrelevant to correct classification. In fact, performance results reveal that when using a small number of training data relative to feature dimensionality, the BDRA outperforms the appropriate class-specific classifier.

Paper Details

Date Published: 4 August 2000
PDF: 8 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395060
Show Author Affiliations
Robert S. Lynch, Naval Undersea Warfare Ctr. (United States)
Peter K. Willett, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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