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Combining Entropy Measures for Anomaly Detection
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 clas...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513228/ https://www.ncbi.nlm.nih.gov/pubmed/33265787 http://dx.doi.org/10.3390/e20090698 |
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author | Muñoz, Alberto Hernández, Nicolás Moguerza, Javier M. Martos, Gabriel |
author_facet | Muñoz, Alberto Hernández, Nicolás Moguerza, Javier M. Martos, Gabriel |
author_sort | Muñoz, Alberto |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7513228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75132282020-11-09 Combining Entropy Measures for Anomaly Detection Muñoz, Alberto Hernández, Nicolás Moguerza, Javier M. Martos, Gabriel Entropy (Basel) Article 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. MDPI 2018-09-12 /pmc/articles/PMC7513228/ /pubmed/33265787 http://dx.doi.org/10.3390/e20090698 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Muñoz, Alberto Hernández, Nicolás Moguerza, Javier M. Martos, Gabriel Combining Entropy Measures for Anomaly Detection |
title | Combining Entropy Measures for Anomaly Detection |
title_full | Combining Entropy Measures for Anomaly Detection |
title_fullStr | Combining Entropy Measures for Anomaly Detection |
title_full_unstemmed | Combining Entropy Measures for Anomaly Detection |
title_short | Combining Entropy Measures for Anomaly Detection |
title_sort | combining entropy measures for anomaly detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513228/ https://www.ncbi.nlm.nih.gov/pubmed/33265787 http://dx.doi.org/10.3390/e20090698 |
work_keys_str_mv | AT munozalberto combiningentropymeasuresforanomalydetection AT hernandeznicolas combiningentropymeasuresforanomalydetection AT moguerzajavierm combiningentropymeasuresforanomalydetection AT martosgabriel combiningentropymeasuresforanomalydetection |