One-class Machines Based on the Coherence Criterion
Résumé
The one-class classification problemis often addressed by solving a constrained quadratic optimization problem, in the same spirit as support vector machines. In this paper, we derive a novel one-class classification approach, by investigating an original sparsification criterion. This criterion, known as the coherence criterion, is based on a fundamental quantity that describes the behavior of dictionaries in sparse approximation problems. The proposed framework allows us to derive new theoretical results. We associate the coherence criterion with a one-class classification algorithm by solving a least-squares optimization problem. We also provide an adaptive updating scheme. Experiments are conducted on real datasets and time series, illustrating the relevance of our approach to existing methods in both accuracy and computational efficiency.
Mots clés
machine learning
sparsity
adaptive filtering
one-class
cybersecurity
approximation theory
constraint handling
dictionaries
least squares approximations
pattern classification
quadratic programming
time series
one-class classification approach
constrained quadratic optimization problem
support vector machine
dictionary
sparse approximation problem
least-square optimization problem
dataset
Support vector machines
Coherence
Kernel
Training
Time series analysis
Optimization
Vectors
kernel methods
one-class classification
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