Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

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.

7 pages, to appear in refereed proceedings of Scalable Machine Learning: Theory and Applications, IEEE BigData 2013

References in corpus (3)

• Automated supervised classification of variable stars I. Methodology
• Analysis of RR Lyrae Stars in the Northern Sky Variability Survey
• New Approaches to Object Classification in Synoptic Sky Surveys

Cited by in corpus (13)

• The Catalina Surveys Periodic Variable Star Catalog
• Immersive and Collaborative Data Visualization Using Virtual Reality Platforms
• Machine Learning in Astronomy: a practical overview
• Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
• Deep-Learnt Classification of Light Curves
• The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression
• Photometric redshifts with machine learning, lights and shadows on a complex data science use case
• Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys
• Modeling Light Curves for Improved Classification
• Nonparametric Transient Classification using Adaptive Wavelets
• Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys
• Leveraging pre-trained vision Transformers for multi-band photometric light curve classification
• AMADA-Analysis of Multidimensional Astronomical Datasets