CaltechAUTHORS
Published October 2013 | Version public
Book Section - Chapter Metadata-only

Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

Creators

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Jet Propulsion Lab
  • 3. ROR icon National Central University
  • 4. ROR icon University of Naples Federico II

Contributors

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.

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.

Additional details

Identifiers

Eprint ID
44473
Resolver ID
CaltechAUTHORS:20140324-134154762

Funding

NSF
AST-0834235
NSF
IIS-1118041
NASA
08-AISR08-0085

Dates

Created
2014-03-26
Created from EPrint's datestamp field
Updated
2021-11-10
Created from EPrint's last_modified field

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Caltech groups
Keck Institute for Space Studies