DocumentCode :
659582
Title :
Feature selection strategies for classifying high dimensional astronomical data sets
Author :
Donalek, Ciro ; Djorgovski, S.G. ; Mahabal, Ashish A. ; Graham, Matthew J. ; Drake, Alan J. ; Fuchs, Thomas J. ; Turmon, Michael J. ; Arun Kumar, A. ; Philip, N. Sajeeth ; Yang, Michael Ting-Chang ; Longo, Giuseppe
Author_Institution :
California Inst. of Technol., Pasadena, CA, USA
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
35
Lastpage :
41
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.
Keywords :
astronomy computing; feature extraction; knowledge acquisition; pattern classification; CRTS; Catalina real-time transient surveys; Kepler Mission; astronomical data set analysis; astronomical problems; feature selection strategies; high dimensional astronomical data set classification; knowledge extraction; multiparametric data sets; scientific fields; Algorithm design and analysis; Astronomy; Cathode ray tubes; Classification algorithms; Feature extraction; Training; Transient analysis; CRTS; astroinformatics; feature selection; machine learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
Type :
conf
DOI :
10.1109/BigData.2013.6691731
Filename :
6691731
Link To Document :
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