A Supremum Norm Based Near Neighbor Search in High Dimensional Spaces

  • Nikolai Sergeev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)

Abstract

This paper presents a new near neighbor search. Feature vectors to be stored do not have to be of equal length. Two feature vectors are getting compared with respect to supremum norm. Time demand to learn a new feature vector does not depend on the number of vectors already learned. A query is formulated not as a single feature vector but as a set of features which overcomes the problem of possible permutation of components in a representation vector. Components of a learned feature vector can be cut out - the algorithm is still capable to recognize the remaining part.

Keywords

Near neighbor search information retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikolai Sergeev
    • 1
  1. 1.Institute for Neural Information ProcessingUniversity of UlmGermany

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