Online One-Class Machines Based on the Coherence Criterion
Résumé
In this paper, we investigate a novel online one-class classification method. We consider a least-squares optimization problem, where the model complexity is controlled by the coherence criterion as a sparsification rule. This criterion is coupled with a simple updating rule for online learning, which yields a low computational demanding algorithm. Experiments conducted on time series illustrate the relevance of our approach to existing methods.
Mots clés
machine learning
sparsity
adaptive filtering
one-class
cybersecurity
learning (artificial intelligence)
least squares approximations
optimisation
pattern classification
online one-class machines
online one-class classification method
least-squares optimization problem
online learning
low computational demanding algorithm
Coherence
Dictionaries
Kernel
Time series analysis
Optimization
Signal processing algorithms
support vector machines
kernel methods
one-class classification
coherence parameter
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