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.
Semi-supervised generalized eigenvalues classification / Viola, Marco; Sangiovanni, Mara; Toraldo, Gerardo; Guarracino, Mario R.. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - 276:1-2(2019), pp. 249-266. [10.1007/s10479-017-2674-1]
Titolo: | Semi-supervised generalized eigenvalues classification | |
Autori: | ||
Data di pubblicazione: | 2019 | |
Rivista: | ||
Citazione: | Semi-supervised generalized eigenvalues classification / Viola, Marco; Sangiovanni, Mara; Toraldo, Gerardo; Guarracino, Mario R.. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - 276:1-2(2019), pp. 249-266. [10.1007/s10479-017-2674-1] | |
Handle: | http://hdl.handle.net/11573/1018712 | |
Appartiene alla tipologia: | 01a Articolo in rivista |
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