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.
@article{arxiv.1310.1976,
title = {Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets},
author = {Ciro Donalek and Arun Kumar A. and S. G. Djorgovski and Ashish A. Mahabal and Matthew J. Graham and Thomas J. Fuchs and Michael J. Turmon and N. Sajeeth Philip and Michael Ting-Chang Yang and Giuseppe Longo},
journal= {arXiv preprint arXiv:1310.1976},
year = {2016}
}
Comments
7 pages, to appear in refereed proceedings of Scalable Machine Learning: Theory and Applications, IEEE BigData 2013