Shrinkage methods for one-class classification. Nader, P., Honeine, P., & Beauseroy, P. In Proc. 23rd European Conference on Signal Processing (EUSIPCO), pages 135-139, Nice, France, 31 August–4 September, 2015.
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Paper doi abstract bibtex
Paper doi abstract bibtex Over the last decades, machine learning techniques have been an important asset for detecting nonlinear relations in data. In particular, one-class classification has been very popular in many fields, specifically in applications where the available data refer to a unique class only. In this paper, we propose a sparse approach for one-class classification problems. We define the one-class by the hypersphere enclosing the samples in the Reproducing Kernel Hilbert Space, where the center of this hypersphere depends only on a small fraction of the training dataset. The selection of the most relevant samples is achieved through shrinkage methods, namely Least Angle Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net. We modify these selection methods and adapt them for estimating the one-class center in the RKHS. We compare our algorithms to well-known one-class methods, and the experimental analysis are conducted on real datasets.
@INPROCEEDINGS{15.eusipco.one_class,
author = "Patric Nader and Paul Honeine and Pierre Beauseroy",
title = "Shrinkage methods for one-class classification",
booktitle = "Proc. 23rd European Conference on Signal Processing (EUSIPCO)",
address = "Nice, France",
year = "2015",
month = "31~" # aug # "--" # "4~" # sep,
pages = {135-139},
acronym = "EUSIPCO",
url_paper = "http://honeine.fr/paul/publi/15.eusipco.oneclass.pdf",
abstract={Over the last decades, machine learning techniques have been an important asset for detecting nonlinear relations in data. In particular, one-class classification has been very popular in many fields, specifically in applications where the available data refer to a unique class only. In this paper, we propose a sparse approach for one-class classification problems. We define the one-class by the hypersphere enclosing the samples in the Reproducing Kernel Hilbert Space, where the center of this hypersphere depends only on a small fraction of the training dataset. The selection of the most relevant samples is achieved through shrinkage methods, namely Least Angle Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net. We modify these selection methods and adapt them for estimating the one-class center in the RKHS. We compare our algorithms to well-known one-class methods, and the experimental analysis are conducted on real datasets.},
keywords={machine learning, one-class, cybersecurity, compressed sensing, Hilbert spaces, learning (artificial intelligence), regression analysis, shrinkage, signal classification, shrinkage methods, one-class classification, machine learning techniques, kernel Hilbert space, least angle regression, least absolute shrinkage, selection operator, elastic net, Kernel, Signal processing algorithms, Training, Support vector machines, Mathematical model, Correlation, Europe, One-class classification, kernel methods, shrinkage methods},
doi={10.1109/EUSIPCO.2015.7362360},
ISSN={2076-1465},
}Downloads: 0
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