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Entropy 2018, 20(9), 698; https://doi.org/10.3390/e20090698

Combining Entropy Measures for Anomaly Detection

1
Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain
2
Department of Computer Science and Statistics, University Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
3
Department of Mathematics and Statistics, Universidad Torcuato Di Tella and CONICET, Buenos Aires C1428BCW, Argentina
*
Authors to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 7 September 2018 / Accepted: 10 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Entropy: From Physics to Information Sciences and Geometry)
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Abstract

The combination of different sources of information is a problem that arises in several situations, for instance, when data are analysed using different similarity measures. Often, each source of information is given as a similarity, distance, or a kernel matrix. In this paper, we propose a new class of methods which consists of producing, for anomaly detection purposes, a single Mercer kernel (that acts as a similarity measure) from a set of local entropy kernels and, at the same time, avoids the task of model selection. This kernel is used to build an embedding of data in a variety that will allow the use of a (modified) one-class Support Vector Machine to detect outliers. We study several information combination schemes and their limiting behaviour when the data sample size increases within an Information Geometry context. In particular, we study the variety of the given positive definite kernel matrices to obtain the desired kernel combination as belonging to that variety. The proposed methodology has been evaluated on several real and artificial problems. View Full-Text
Keywords: entropy kernel; kernel combination; Karcher mean; anomaly detection; functional data entropy kernel; kernel combination; Karcher mean; anomaly detection; functional data
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Muñoz, A.; Hernández, N.; Moguerza, J.M.; Martos, G. Combining Entropy Measures for Anomaly Detection. Entropy 2018, 20, 698.

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