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International Conference on Analysis of Images, Social Networks and Texts

AIST 2019: Analysis of Images, Social Networks and Texts pp 73–80Cite as

Fast Nearest-Neighbor Classifier Based on Sequential Analysis of Principal Components

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11832))

Abstract

In this paper we improve the speed of the nearest neighbor classifiers of a set of points based on sequential analysis of high-dimensional feature vectors. Each input object is associated with a sequence of principal component scores of aggregated features extracted by deep neural network. The number of components in each element of this sequence is dynamically chosen based on explained proportion of total variance for the training set. We propose to process the next element with higher explained variance only if the decision for the current element is unreliable. This reliability is estimated by matching of the ratio of the minimum distance and all other distances with a certain threshold. Experimental study for face recognition with the Labeled Faces in the Wild and YouTube Faces datasets demonstrates the decrease of running time up to 10 times when compared to conventional instance-based learning.

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Acknowledgments

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE).

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russian Federation

    Anastasiia D. Sokolova & Andrey V. Savchenko

Authors
  1. Anastasiia D. Sokolova
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  2. Andrey V. Savchenko
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Corresponding author

Correspondence to Anastasiia D. Sokolova .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre-lès-Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  12. Ca Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Sokolova, A.D., Savchenko, A.V. (2019). Fast Nearest-Neighbor Classifier Based on Sequential Analysis of Principal Components. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-37334-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37333-7

  • Online ISBN: 978-3-030-37334-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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