NDLI: Large margin nearest neighbor classifiers

Content Provider	IEEE Xplore Digital Library
Author	Domeniconi, C. Gunopulos, D. Jing Peng
Copyright Year	1990
Abstract	The nearest neighbor technique is a simple and appealing approach to addressing classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. The employment of a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. We propose a technique that computes a locally flexible metric by means of support vector machines (SVMs). The decision function constructed by SVMs is used to determine the most discriminant direction in a neighborhood around the query. Such a direction provides a local feature weighting scheme. We formally show that our method increases the margin in the weighted space where classification takes place. Moreover, our method has the important advantage of online computational efficiency over competing locally adaptive techniques for nearest neighbor classification. We demonstrate the efficacy of our method using both real and simulated data.
Sponsorship	IEEE Computational Intelligence Society
Page Count	11
File Size	492451
Starting Page	899
Ending Page	909
File Format	PDF
ISSN	10459227
Volume Number	16
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nearest neighbor searches Support vector machines Support vector machine classification Error analysis Employment Computational efficiency Computational modeling Engineering profession Software engineering Computer science support vector machines (SVMs) Feature relevance margin nearest neighbor classification
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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