Regularized Kernel Algorithms for Support Estimation
Alessandro Rudi, Ernesto De Vito, Alessandro Verri, Francesca Odone · Frontiers in Applied Mathematics and Statistics · 2017
In the framework of non-parametric support estimation, we study the statistical properties of an estimator defined by means of Kernel Principal Component Analysis (KPCA). In the context of anomaly/novelty detection the algorithm was first introduced by Hoffmann in 2007. We also extend to above analysis to a larger class of set estimators defined in terms of a filter function.