Refereed full papers (journals, book chapters, international conferences)

2000

@inproceedings{DGP2000,
	vgclass =	{refpap},
	author =	{Carlotta Domeniconi and Dimitrios Gunopulos and Jing Peng},
	title =	{Adaptive Metric Nearest Neighbor Classification},
	booktitle =	{Proceedings of the 2000 IEEE Conference on Computer
	Vision and Pattern Recognition (CVPR2000)},
	address =	{Hilton Head, SC, USA},
	organization =	{IEEE Computer Society},
	month =	{June~13--15},
	year =	{2000},
	url =	{http://dx.doi.org/10.1109/CVPR.2000.855863},
	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.},
}