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On the behavior of the infinite restricted boltzmann machine for clustering
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2949-4123
2018 (English)In: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing / [ed] Hisham M. Haddad, Roger L. Wainwright, Richard Chbeir, New York, NY, USA: Association for Computing Machinery (ACM), 2018, p. 461-470Conference paper, Published paper (Refereed)
Abstract [en]

Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2018. p. 461-470
Keywords [en]
clustering, unsupervised, machine learning, restricted boltzmann machine
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-16505DOI: 10.1145/3167132.3167183ISI: 000455180700067Scopus ID: 2-s2.0-85050522612ISBN: 978-1-4503-5191-1 (print)OAI: oai:DiVA.org:his-16505DiVA, id: diva2:1271623
Conference
SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-02-14Bibliographically approved

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Huhnstock, Nikolas AlexanderKarlsson, AlexanderRiveiro, MariaSteinhauer, H. Joe

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