Adaptive Feature Selection in Pattern Recognition and Ultra-Wideband Radar Signal Analysis

Citation

Jiang, Hao (2008) Adaptive Feature Selection in Pattern Recognition and Ultra-Wideband Radar Signal Analysis. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/7NR6-AR24. https://resolver.caltech.edu/CaltechETD:etd-05302008-134607

Abstract

Feature selection from measured data aims to extract informative features to reveal the statistic or stochastic mechanism underlying the complicated or high dimensional original data. In this thesis, the feature selection problem is probed under two situations, one is pattern recognition and the other is ultra-wideband radar signal analysis.

Classical pattern recognition methods select features by their ability to separate the multiple classes with certain gauge measure. The deficiency in this general strategy is its lack of adaptation in specific situations. This deficiency may be overcome by viewing the selected features as a function of not only the training samples but also the unlabeled test data. From this perspective, this thesis proposes an adaptive sequential feature selection algorithm which utilizes an information-theoretic measure to reduce the classification task complexity sequentially, and finally outputs the probabilistic classification result and its variation level. To verify the potential advantage of this algorithm, this thesis applies it to one important problem of neural prosthesis, which concerns decoding a finite number of classes, intended reach directions, from recordings of neural activities in the Parietal Reach Region of one rhesus monkey. Experimental results show that the classification scheme of combining the adaptive sequential feature selection algorithm and the information fusion method outperforms some classical pattern recognition rules, such as the nearest neighbor rule and support vector machine, in decoding performance.

The second scenario in this thesis targets developing a human presence and motion pattern detector through ultra-wideband radar signal analysis. To augment the detection robustness, both static and dynamic features should be utilized. The static features reflect the information of target geometry and its variability, while the dynamic features extract the temporal structure among radar scans. The problem of static feature selection is explored in this thesis, which utilizes the Procrustes shape analysis to generate the representative template for the target images, and makes statistical inference in the tangent space through the Hotelling one sample test. After that, the waveform shape variation structure is decomposed in the tangent space through the principal component analysis. The selected principal components not only accentuate the prominent dynamics of the target motion, but also generate another informative classification feature.

Thesis Files