Locally adaptive metric nearest-neighbor classification

Carlotta Domeniconi, Jing Peng, Dimitrios Gunopulos

Research output: Contribution to journalArticle

234 Scopus citations

Abstract

Nearest-neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest-neighbor rule. We propose a locally adaptive nearest-neighbor classification method to try to minimize bias. We use a Chi-squared distance analysis to compute a flexible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most influential ones. As a result, the class conditional probabilities are smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using both simulated and real-world data.

Original languageEnglish
Pages (from-to)1281-1285
Number of pages5
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume24
Issue number9
DOIs
StatePublished - 1 Sep 2002

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Keywords

  • Chi-squared distance
  • Classification
  • Feature relevance
  • Nearest neighbors

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