LDA/SVM driven nearest neighbor classification

Jing Peng, Douglas R. Heisterkamp, H. K. Dai

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

Nearest neighbor (NN) classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The NN rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local support vector machine learning to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of datasets.

Original languageEnglish
Pages (from-to)940-942
Number of pages3
JournalIEEE Transactions on Neural Networks
Volume14
Issue number4
DOIs
StatePublished - 1 Jul 2003

Keywords

  • Classification
  • Linear discriminant analysis (LDA)
  • Nearest neighbor
  • Support vector machine (SVM)

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