Dimensionality Reduction with Unsupervised Nearest Neighbors

  • Oliver┬áKramer

Part of the Intelligent Systems Reference Library book series (ISRL, volume 51)

Table of contents

  1. Front Matter
    Pages 1-15
  2. Oliver Kramer
    Pages 1-9
  3. Foundations

    1. Front Matter
      Pages 11-11
    2. Oliver Kramer
      Pages 13-23
    3. Oliver Kramer
      Pages 25-32
    4. Oliver Kramer
      Pages 33-52
  4. Unsupervised Nearest Neighbors

    1. Front Matter
      Pages 53-53
    2. Oliver Kramer
      Pages 55-73
    3. Oliver Kramer
      Pages 75-91
    4. Oliver Kramer
      Pages 93-111
  5. Conclusions

    1. Front Matter
      Pages 113-113
    2. Oliver Kramer
      Pages 115-118
  6. Back Matter
    Pages 119-129

About this book


This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.



Computational Intelligence Evolutionary Computation Self-Adaptive Heuristics

Authors and affiliations

  • Oliver┬áKramer
    • 1
  1. 1., Computer Science DepartmentCarl von Ossietzky University OldenburgOldenburgGermany

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-38651-0
  • Online ISBN 978-3-642-38652-7
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site