Optimistic semi-supervised least squares classification

Conference paper (2016)

Authors

J.H. Krijthe Leiden University Medical Center, Pattern Recognition and Bioinformatics -

M. Loog University of Copenhagen, Pattern Recognition and Bioinformatics -

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Optimization Linear programming Convergence Encoding Labeling Training Semisupervised learning

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http://resolver.tudelft.nl/uuid:501cc27e-8ad3-4daf-a6f6-24291d4decc4

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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different objective functions. The resulting soft-label approach is related to an idea about dealing with missing data that dates back to the 1930s. We show that the soft-label variant typically outperforms the hard-label variant on benchmark datasets and partially explain this behaviour by studying the relative difficulty of finding good local minima for the corresponding objective functions.