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Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

Donalek, Ciro and Djorgovski, S. G. and Mahabal, Ashish A. and Graham, Matthew J. and Drake, Andrew J. and Fuchs, Thomas J. and Turmon, Michael J. and Kumar, A. Arun and Philip, N. Sajeeth and Yang, Michael Ting-Chang and Longo, Giuseppe (2013) Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets. In: Big Data, 2013 IEEE International Conference. IEEE , New York, NY, pp. 35-45. ISBN 978-1-4799-1292-6. http://resolver.caltech.edu/CaltechAUTHORS:20140324-134154762

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Abstract

The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6691731PublisherArticle
http://dx.doi.org/10.1109/BigData.2013.6691731DOIArticle
ORCID:
AuthorORCID
Djorgovski, S. G.0000-0002-0603-3087
Additional Information:© 2013 IEEE. Conference Date(s): 6-9 Oct. 2013. S.G.D., C.D., A.A.M., and M.J.G acknowledge a partial support from the NSF grants AST-0834235 and IIS-1118041, and the NASA grant 08-AISR08-0085. Some of the work reported here benefited from the discussions during a study and the workshops organized by the Keck Institute for Space Studies at Caltech.
Group:Keck Institute for Space Studies
Funders:
Funding AgencyGrant Number
NSFAST-0834235
NSFIIS-1118041
NASA08-AISR08-0085
Subject Keywords:astroinformatics; machine learning; feature selection; CRTS
Record Number:CaltechAUTHORS:20140324-134154762
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20140324-134154762
Official Citation:Donalek, C.; Djorgovski, S.G.; Mahabal, A.A.; Graham, M.J.; Drake, A.J.; Fuchs, T.J.; Turmon, M.J.; Arun Kumar, A.; Philip, N.S.; Yang, M.T.-C.; Longo, G., "Feature selection strategies for classifying high dimensional astronomical data sets," Big Data, 2013 IEEE International Conference on , vol., no., pp.35,41, 6-9 Oct. 2013 doi: 10.1109/BigData.2013.6691731
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:44473
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:26 Mar 2014 20:43
Last Modified:12 Sep 2017 22:41

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