Minimizing Nearest Neighbor Classification Error for Nonparametric Dimension Reduction
Wei Bian, Tianyi Zhou, Aleix M. Martı́nez, George Baciu, Dacheng Tao · IEEE Transactions on Neural Networks and Learning Systems · 2014
In this brief, we show that minimizing nearest neighbor classification error (MNNE) is a favorable criterion for supervised linear dimension reduction (SLDR). We prove that MNNE is better than maximizing mutual information in the sense of being a proxy of the Bayes optimal criterion. Based on kernel density estimation, we derive a nonparametric algorithm for MNNE. Experiments on benchmark data sets show the superiority of MNNE over existing nonparametric SLDR methods.