Bounding the complexity of structural expectation-maximization

Benjumeda Barquita, Marco Alberto and Luengo Sánchez, Sergio and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2018). Bounding the complexity of structural expectation-maximization. In: "35th International Conference on Machine Learning (ICML 2018)", 10-15 Jul 2018, Estocolmo, Suecia. ISBN 978-1-5108-6796-3. pp. 1-3.

Description

Title: Bounding the complexity of structural expectation-maximization
Author/s:
  • Benjumeda Barquita, Marco Alberto
  • Luengo Sánchez, Sergio
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Presentation at Congress or Conference (Article)
Event Title: 35th International Conference on Machine Learning (ICML 2018)
Event Dates: 10-15 Jul 2018
Event Location: Estocolmo, Suecia
Title of Book: Proceedings of the 35th International Conference on Machine Learning (ICML 2018)
Date: 2018
ISBN: 978-1-5108-6796-3
Volume: 80
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Structural expectation-maximization is the most common approach to address the problem of learning Bayesian networks from incomplete datasets. Its main limitation is that its computational cost is usually extremely demanding when the number of variables or the number of instances is not small. The bottleneck of this algorithm is the inference complexity of the model candidates. Thus, bounding the inference complexity of each Bayesian network during the learning process is key to make structural expectation-maximization efficient. In this paper, we propose a tractable adaptation of structural expectation-maximization and perform experiments to analyze its performance.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes
Horizon 2020785907HBP SGA2UnspecifiedHuman Brain Project Specific Grant Agreement 2
Government of SpainTIN2013-41592-PBES-2014-068637Universidad Politécnica de MadridUnspecified

More information

Item ID: 54654
DC Identifier: http://oa.upm.es/54654/
OAI Identifier: oai:oa.upm.es:54654
Official URL: http://cig.fi.upm.es/articles/2018/Benjumeda_Luengo-Bounding_the_Complexity_of_Structural_Expectation-Maximization.pdf
Deposited by: Memoria Investigacion
Deposited on: 25 Apr 2019 09:41
Last Modified: 25 Apr 2019 09:41
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