title: Minimizing nearest neighbor classification error for nonparametric dimension reduction.
abstract: 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.
authors: Wei
authors: W
authors: Bian
authors: Tianyi
authors: T
authors: Zhou
authors: Aleix M
authors: AM
authors: Martinez
authors: George
authors: G
authors: Baciu
authors: Dacheng
authors: D
authors: Tao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D016014:Linear Models; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*