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Semi-supervised generalized eigenvalues classification.
- Source :
-
Annals of Operations Research . May2019, Vol. 276 Issue 1/2, p249-266. 18p. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *LAPLACIAN matrices
*EIGENVALUES
*CLASSIFICATION
*ALGORITHMS
*DATA analysis
Subjects
Details
- Language :
- English
- ISSN :
- 02545330
- Volume :
- 276
- Issue :
- 1/2
- Database :
- Academic Search Index
- Journal :
- Annals of Operations Research
- Publication Type :
- Academic Journal
- Accession number :
- 135796890
- Full Text :
- https://doi.org/10.1007/s10479-017-2674-1