Class-specific feature selection based on uniform dirichlet priors

Robert S. Lynch Jr.; Peter K. Willett

doi:10.1117/12.395060

4 August 2000 Class-specific feature selection based on uniform dirichlet priors

Proceedings Volume 4052, Signal Processing, Sensor Fusion, and Target Recognition IX; (2000) https://doi.org/10.1117/12.395060
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Robert S. Lynch Jr. and Peter K. Willett "Class-specific feature selection based on uniform dirichlet priors", Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); https://doi.org/10.1117/12.395060

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