The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning

Conference paper (2018)

Authors

J.H. Krijthe Radboud Universiteit Nijmegen
M. Loog University of Copenhagen, Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that are decreasing, it is impossible to construct any semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss on the labeled and unlabeled data. For convex margin-based loss functions that also increase, we demonstrate safe improvements are possible

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