Mahalanobis-Based One-Class Classification
Résumé
Machine learning techniques have become very popular in the past decade for detecting nonlinear relations in large volumes of data. In particular, one-class classification algorithms have gained the interest of the researchers when the available samples in the training set refer to a unique/single class. In this paper, we propose a simple one-class classification approach based on the Mahalanobis distance. We make use of the advantages of kernel whitening and KPCA in order to compute the Mahalanobis distance in the feature space, by projecting the data into the subspace spanned by the most relevant eigenvectors of the covariance matrix. We also propose a sparse formulation of this approach. The tests are conducted on simulated data as well as on real data.
Mots clés
machine learning
one-class
cybersecurity
learning (artificial intelligence)
pattern classification
Mahalanobis-based one-class classification
machine learning techniques
nonlinear relation detection
Mahalanobis distance
kernel whitening
KPCA
feature space
covariance matrix eigenvectors
Kernel
Training
Covariance matrices
Support vector machines
Pipelines
Matrix decomposition
Eigenvalues and eigenfunctions
Kernel methods
one-class classification
| Origine | Fichiers produits par l'(les) auteur(s) |
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