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The Peaking Phenomenon in Semi-supervised Learning

  • Jesse H. KrijtheEmail author
  • Marco Loog
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a similar but more pronounced version of this phenomenon also occurs in the semi-supervised setting, where instead of labeled objects, unlabeled objects are added to the training set. We explain why the learning curve has a more steep incline and a more gradual decline in this setting through simulation studies and by applying an approximation of the learning curve based on the work by Raudys and Duin.

Keywords

Semi-supervised learning Peaking Least squares classifier Pseudo-inverse 

Notes

Acknowledgements

This work was funded by project P23 of the Dutch public/private research network COMMIT.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Pattern Recognition LaboratoryDelft University of TechnologyDelftNetherlands
  2. 2.Department of Molecular EpidemiologyLeiden University Medical CenterLeidenNetherlands
  3. 3.The Image SectionUniversity of CopenhagenCopenhagenDenmark

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