Support Measure Data Description for group anomaly detection

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.
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Communication dans un congrès
ODDx3 Workshop on Outlier Definition, Detection, and Description at the 21st ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD2015), Aug 2015, Sydney, Australia
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Soumis le : vendredi 10 juin 2016 - 17:46:56
Dernière modification le : jeudi 7 décembre 2017 - 12:34:10

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Jorge Guevara, Stéphane Canu, R Hirata. Support Measure Data Description for group anomaly detection. ODDx3 Workshop on Outlier Definition, Detection, and Description at the 21st ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD2015), Aug 2015, Sydney, Australia. 〈hal-01330487〉

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