Abstract
Astroinformatics is an interdisciplinary field of science that applies modern computational tools to the solution of astronomical problems. One relevant subarea is the use of machine learning for analysis of large astronomical repositories and surveys. In this paper we describe a case study based on the classification of variable Cepheid stars using domain adaptation techniques; our study highlights some of the emerging challenges posed by astroinformatics.
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Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Wortman, J.: A Theory of Learning from Different Domains. Machine Learning, Special Issue on Learning From Multiple Sources 79, 151–175 (2010)
Ivezic, Z., Connolly, A.J., VanderPlas, J.T., Gray, A.: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Princeton Series in Modern Observational Astronomy. Princeton University Press (2014)
LSST Science Book, version 2.0, 245 authors. http://www.lsst.org/lsst/scibook
Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N. D.: Dataset Shift in Machine Learning. MIT Press (2009)
Vilalta, R., Dhar Gupta, K., Macri, L.: A Machine Learning Approach to Cepheid Variable Star Classification using Data Alignment and Maximum Likelihood. Astronomy and Computing 2, 46–53 (2013). Elsevier
Vilalta, R., Dhar Gupta, K., Macri, L.: Domain adaptation under data misalignment: an application to cepheid variable star classification. In: The 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden (2014)
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© 2015 Springer International Publishing Switzerland
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Vilalta, R., Dhar Gupta, K., Mahabal, A. (2015). Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_22
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DOI: https://doi.org/10.1007/978-3-319-23461-8_22
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