IEEE Trans Neural Netw Learn Syst - Robust Novelty Detection via Worst Case CVaR Minimization.

Tópicos

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Resumo

Novelty detection models aim to find the minimum volume set covering a given probability mass. This paper proposes a robust single-class support vector machine (SSVM) for novelty detection, which is mainly based on the worst case conditional value-at-risk minimization. By assuming that every input is subject to an uncertainty with a specified symmetric support, this robust formulation results in a maximization term that is similar to the regularization term in the classical SSVM. When the uncertainty set is l1 -norm, l8 -norm or box, its training can be reformulated to a linear program; while the uncertainty set is l2 -norm or ellipsoidal, its training is a tractable second-order cone program. The proposed method has a nice consistent statistical property. As the training size goes to infinity, the estimated normal region converges to the true provided that the magnitude of the uncertainty set decreases in a systematic way. The experimental results on three data sets clearly demonstrate its superiority over three benchmark models.

Resumo Limpo

novelti detect model aim find minimum volum set cover given probabl mass paper propos robust singleclass support vector machin ssvm novelti detect main base worst case condit valueatrisk minim assum everi input subject uncertainti specifi symmetr support robust formul result maxim term similar regular term classic ssvm uncertainti set l norm l norm box train can reformul linear program uncertainti set l norm ellipsoid train tractabl secondord cone program propos method nice consist statist properti train size goe infin estim normal region converg true provid magnitud uncertainti set decreas systemat way experiment result three data set clear demonstr superior three benchmark model

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