Shrinkage methods for one-class classification
Résumé
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.
Mots clés
machine learning
one-class
cybersecurity
compressed sensing
Hilbert spaces
learning (artificial intelligence)
regression analysis
shrinkage
signal classification
shrinkage methods
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
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...