Adaptive kernel metric nearest neighbor classification

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

Research output: Contribution to journalArticleResearchpeer-review

30 Citations (Scopus)

Abstract

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions due to the curse-of-dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose an adaptive nearest neighbor classification method to try to minimize bias. We use quasiconformal transformed kernels to compute neighborhoods over which the class probabilities tend to be more homogeneous. As a result, better classification performance can be expected. The efficacy of our method is validated and compared against other competing techniques using a variety of data sets.

Original languageEnglish
Pages (from-to)33-36
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume16
Issue number3
StatePublished - 1 Dec 2002

Cite this

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Adaptive kernel metric nearest neighbor classification. / Peng, Jing; Heisterkamp, Douglas R.; Dai, H. K.

In: Proceedings - International Conference on Pattern Recognition, Vol. 16, No. 3, 01.12.2002, p. 33-36.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Peng, Jing

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AU - Dai, H. K.

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N2 - Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions due to the curse-of-dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose an adaptive nearest neighbor classification method to try to minimize bias. We use quasiconformal transformed kernels to compute neighborhoods over which the class probabilities tend to be more homogeneous. As a result, better classification performance can be expected. The efficacy of our method is validated and compared against other competing techniques using a variety of data sets.

AB - Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions due to the curse-of-dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose an adaptive nearest neighbor classification method to try to minimize bias. We use quasiconformal transformed kernels to compute neighborhoods over which the class probabilities tend to be more homogeneous. As a result, better classification performance can be expected. The efficacy of our method is validated and compared against other competing techniques using a variety of data sets.

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