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Fast Design of Reduced-Complexity Nearest-Neighbor Classifiers Using Triangular Inequality
May 1998 (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.

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Index Terms:
Nearest-neighbor classifier, triangular inequality, computational complexity, NIST database, fast design.
Citation:
Eel-Wan Lee, Soo-Ik Chae, "Fast Design of Reduced-Complexity Nearest-Neighbor Classifiers Using Triangular Inequality," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 562-566, May 1998, doi:10.1109/34.682187
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