Approximate k-nearest neighbor method

Sirpa Saarinen; George Cybenko

doi:10.1117/12.150579

1 September 1993 Approximate k-nearest neighbor method

Sirpa Saarinen, George Cybenko

Proceedings Volume 1962, Adaptive and Learning Systems II; (1993) https://doi.org/10.1117/12.150579
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

Memory based techniques are becoming increasingly popular as learning methods. The k- nearest neighbor method has often been mentioned as one of the best learning methods but it has two basic drawbacks: the large storage demand and the often tedious search of the neighbors. In this paper, we present a method for approximating k-th nearest neighbor methods by using a hybrid kernel function and explicit data representation and thus reducing the amount of data used. This method will not use the correct nearest neighbors to a point but will use an average measure of them. Finding the real neighbors is not always needed for accurate classification but finding a few nearby points is sufficient for most cases.

Citation Download Citation

Sirpa Saarinen and George Cybenko "Approximate k-nearest neighbor method", Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); https://doi.org/10.1117/12.150579

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