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Fast design of reduced complexity nearest-neighbor classifiers using triangular inequality

Cited 12 time in Web of Science Cited 14 time in Scopus
Authors
Lee, Eelwan; Chae, Soo-Ik
Issue Date
1998
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 5, pp. 562-566
Abstract
In this paper, we propose a method of designing a reduced complexity nearest-neighbor (RCNN) classifier with near-minimal computational complexity from a given nearest-neighbor classifier that has high input dimensionality and a large number of class vectors. We applied our method to the classification problem of handwritten numerals in the NIST database. If the complexity of the RCNN classifier is normalized to that of the given classifier, the complexity of the derived classifier is 62 percent, 2 percent higher than that of the optimal classifier. This was found using the exhaustive search.
ISSN
0162-8828
Language
English
URI
http://hdl.handle.net/10371/21263
DOI
https://doi.org/10.1109/34.682187
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Journal Papers (저널논문_전기·정보공학부)
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