Adaptive metric nearest neighbor classification

Carlotta Domeniconi, Jing Peng, Dimitrios Gunopulos

Research output: Contribution to journalConference articlepeer-review

9 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 tend to be 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 a variety of simulated and real world data.

Original languageEnglish
Pages (from-to)517-522
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - 2000
EventIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 - Hilton Head Island, SC, USA
Duration: 13 Jun 200015 Jun 2000

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