Minimizing Nearest Neighbor Classification Error for Nonparametric Dimension Reduction

Author

Wei Bian ; Tianyi Zhou ; Martinez, Ana Milena ; Baciu, George ; Dacheng Tao

Author_Institution

Centre of Quantum Comput. & Intell. Syst., Univ. of Technol., Sydney, NSW, Australia

Volume

25

Issue

8

fYear

2014

fDate

Aug. 2014

Firstpage

1588

Lastpage

1594

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.

Keywords

Bayes methods; minimisation; nonparametric statistics; pattern classification; Bayes optimal criterion; MNNE; SLDR; benchmark datasets; kernel density estimation; minimizing nearest neighbor classification error; nonparametric dimension reduction algorithm; supervised linear dimension reduction; Artificial neural networks; Bandwidth; Entropy; Kernel; Manifolds; Mutual information; Training; Bayes optimal criterion; nearest neighbor classification error (NN error); nonparametric methods; supervised linear-dimension reduction (SLDR); supervised linear-dimension reduction (SLDR).;

fLanguage

English

Journal_Title

Neural Networks and Learning Systems, IEEE Transactions on

Publisher

ieee

ISSN

2162-237X

Type

jour

DOI

10.1109/TNNLS.2013.2294547

Filename

6698335