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High-Dimensional Binary Pattern Classification by Scalar Neural Network Tree

  • Vladimir Kryzhanovsky
  • Magomed Malsagov
  • Juan Antonio Clares Tomas
  • Irina Zhelavskaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

The paper offers an algorithm (SNN-tree) that extends the binary tree search algorithm so that it can deal with distorted input vectors. Perceptrons are the tree nodes. The algorithm features an iterative solution search and stopping criterion. Unlike the SNN-tree algorithm, popular methods (LSH, k-d tree, BBF-tree, spill-tree) stop working as the dimensionality of the space grows (N > 1000). With such high dimensionality, our algorithm works 7 times faster than the exhaustive search algorithm.

Keywords

Nearest neighbor searching perceptron search tree hierarchical classifier multi-class classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vladimir Kryzhanovsky
    • 1
  • Magomed Malsagov
    • 1
  • Juan Antonio Clares Tomas
    • 2
  • Irina Zhelavskaya
    • 3
  1. 1.Scientific Research Institute for System AnalysisRussian Academy of SciencesRussia
  2. 2.Institute of Secondary Education: IES SANJEMurciaSpain
  3. 3.Skolkovo Institute of Science and TechnologyMoscowRussia

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