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1 VISION - computer vision research group 2 LITIS - Laboratoire d-Informatique, de Traitement de l-Information et des Systèmes 3 USP - Universidade de São Paulo São Paulo

Abstract : We address the problem of learning a data description model from a dataset containing probability measures as observations. We estimate the data description model by optimizing volume-sets of probability measures where each volume-set is defined as a set of probability measures whose representative functions in a reproducing kernel Hilbert space RKHS belong to an enclosing ball. We present three data description models, which are functions in a RKHS depending only on some probability measures, named support measures in analogy to support vectors. An advantage of the method is that we do not consider any particular form for the probability measures. We validate our method in the task of group anomaly detection, with artificial and real datasets.

Keywords : Kernel on distributions One-class classification support vec-tor data description embedding of probability measures mean map group anomaly detection MV-set

Author: Jorge Guevara - Stéphane Canu - R Hirata -



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