Semi-supervised generalized eigenvalues classification

Marco Viola (), Mara Sangiovanni (), Gerardo Toraldo () and Mario R. Guarracino ()
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Marco Viola: Sapienza University of Rome
Mara Sangiovanni: National Research Council of Italy
Gerardo Toraldo: University of Naples Federico II
Mario R. Guarracino: National Research Council of Italy

Annals of Operations Research, 2019, vol. 276, issue 1, No 12, 249-266

Abstract: Abstract Supervised classification is one of the most powerful techniques to analyze data, when a-priori information is available on the membership of data samples to classes. Since the labeling process can be both expensive and time-consuming, it is interesting to investigate semi-supervised algorithms that can produce classification models taking advantage of unlabeled samples. In this paper we propose LapReGEC, a novel technique that introduces a Laplacian regularization term in a generalized eigenvalue classifier. As a result, we produce models that are both accurate and parsimonious in terms of needed labeled data. We empirically prove that the obtained classifier well compares with other techniques, using as little as 5% of labeled points to compute the models.

Keywords: Semi-supervised classification; Laplacian regularization; Manifold regularization; Generalized eigenvalues classifiers (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10479-017-2674-1

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