The one-class classification problem is 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.
In REF a "coherence" based sparsification criterion is used in the context of one-class classification.
One-class machines based on the coherence criterion
2012 IEEE Statistical Signal Processing Workshop (SSP)
2012 IEEE Statistical Signal Processing Workshop (SSP)
Mathematics Computer Science