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On the behavior of the infinite restricted boltzmann machine for clustering
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0003-2973-3112
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0003-2900-9335
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0003-2949-4123
2018 (engelsk)Inngår i: 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, s. 461-470Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
New York, NY, USA: Association for Computing Machinery (ACM), 2018. s. 461-470
Emneord [en]
clustering, unsupervised, machine learning, restricted boltzmann machine
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifikatorer
URN: urn:nbn:se:his:diva-16505DOI: 10.1145/3167132.3167183ISI: 000455180700067Scopus ID: 2-s2.0-85050522612ISBN: 978-1-4503-5191-1 (tryckt)OAI: oai:DiVA.org:his-16505DiVA, id: diva2:1271623
Konferanse
SAC 18 The 33rd Annual ACM Symposium on Applied Computing, Pau, France, April 9-13, 2018
Tilgjengelig fra: 2018-12-17 Laget: 2018-12-17 Sist oppdatert: 2019-02-14bibliografisk kontrollert

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