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Stochastic Feature Selection With Distributed Feature Spacing For Hyperspectral Data, Jeffrey D. Clark, Michael J. Mendenhall, Gilbert L. Peterson
Stochastic Feature Selection With Distributed Feature Spacing For Hyperspectral Data, Jeffrey D. Clark, Michael J. Mendenhall, Gilbert L. Peterson
Faculty Publications
Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspectral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which increases the chance of avoiding locally optimum solutions. Second, we exploit local cross correlation pair-wise amongst classes of interest to select suitable features for class discrimination. Third, we adopt the concept of distributed spacing from the multi-objective optimization community to distribute features …