A Nearest Neighbor Method Using Bisectors

  • Mineichi Kudo
  • Hideyuki Imai
  • Akira Tanaka
  • Tetsuya Murai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


A novel algorithm for finding the nearest neighbor was proposed. According to the development of modern technology, the demand is increasing in large-scale datasets with a large number of samples and a large number of features. However, almost all sophisticated algorithms proposed so far are effective only in a small number of features, say, up to 10. This is because in a high-dimensional space many pairs of samples share a same distance. Then the naive algorithm outperforms the others. In this study, we considered to utilize a sequential information of distances obtained by the examined training samples. Indeed, a combinatorial information of examined samples was used as bisectors between possible pairs of them. With this algorithm, a query is processed in O(αβ nd) for n samples in a d-dimensional space and for α,β < 1, in expense of a preprocessing time and space in O(n 2). We examined the performance of the algorithm.


Training Sample Distance Calculation Query Point Query Time Ball Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mineichi Kudo
    • 1
  • Hideyuki Imai
    • 1
  • Akira Tanaka
    • 1
  • Tetsuya Murai
    • 1
  1. 1.Division of Systems and Information Engineering, Graduate School of EngineeringHokkaido UniversitySapporoJapan

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