Localized Exploratory Projection Pursuit,
BROWN UNIV PROVIDENCE RI CENTER FOR NEURAL SCIENCE
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Based on CART, we introduce a recursive partitioning method for high dimensional space which partitions the data using low dimensional features. The low dimensional features are extracted via an exploratory projection pursuit EPP method, localized to each node in the tree. In addition, we present an exploratory splitting rule that is potentially less biased to the training data. This leads to a nonparametric classifier for high dimensional space that has local feature extractors optimized to different regions in the input space.
- Statistics and Probability
- Information Science