Prototype-based analysis of GAMA galaxy catalogue data

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2 Citaten (Scopus)
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We present a prototype-based machine learning analysis of labeled galaxy catalogue data containing parameters from the Galaxy and Mass Assembly (GAMA) survey. Using both an unsupervised and supervised method, the Self-Organizing Map and Generalized Relevance Matrix Learning Vec- tor Quantization, we find that the data does not fully support the popular visual-inspection-based galaxy classification scheme employed to categorize the galaxies. In particular, only one class, the Little Blue Spheroids, is consistently separable from the other classes. In a proof-of-concept experiment, we present the galaxy parameters that are most discriminative for this class.
Originele taal-2English
TitelESANN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
RedacteurenMichel Verleysen
Plaats van productieLouvain-La-Neuve
UitgeverijCiaco -
Aantal pagina's6
ISBN van elektronische versie978-287-587-048-3
ISBN van geprinte versie978-287-587-047-6
StatusPublished - 24-apr.-2018
EvenementSpecial Session at ESANN 2018: Machine Learning and Data Analysis in Astroinformatics - Brugge, Belgium
Duur: 25-apr.-201827-apr.-2018


ConferenceSpecial Session at ESANN 2018
Internet adres

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