Minimizing nearest neighbor classification error for nonparametric dimension reduction

Wei Bian; Tianyi Zhou; Aleix M. Martinez; George Baciu; Dacheng Tao

doi:10.1109/TNNLS.2013.2294547

Minimizing nearest neighbor classification error for nonparametric dimension reduction

Wei Bian, Tianyi Zhou, Aleix M. Martinez, George Baciu, Dacheng Tao

Research output: Journal article publication › Journal article › Academic research › peer-review

5 Citations (Scopus)

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.

Original language	English
Article number	6698335
Pages (from-to)	1588-1594
Number of pages	7
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	25
Issue number	8
DOIs	https://doi.org/10.1109/TNNLS.2013.2294547
Publication status	Published - 1 Jan 2014

Keywords

Bayes optimal criterion
nearest neighbor classification error (NN error)
nonparametric methods
supervised linear-dimension reduction (SLDR)

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/TNNLS.2013.2294547

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KW - supervised linear-dimension reduction (SLDR)

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Minimizing nearest neighbor classification error for nonparametric dimension reduction

Abstract

Keywords

ASJC Scopus subject areas

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