Variable Star Classification Using Multi-View Metric Learning

Abstract

Our multi-view metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multi-faceted feature space, thus, eliminating the need to combine feature representations prior to fitting the machine learning model. We also demonstrate how to extend standard multi-view learning, which employs multiple vectorized views, to the matrix-variate case which allows very novel variable star signature representations. The performance of our proposed methods is evaluated on the UCR Starlight and LINEAR datasets. Both the vector and matrix-variate versions of our multi-view learning framework perform favorably --- demonstrating the ability to discriminate variable star categories.

16 pages, 11 figures

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Cited by in corpus (6)

• The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
• On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
• Classifying Kepler light curves for 12,000 A and F stars using supervised feature-based machine learning
• Image-based Classification of Variable Stars: First Results from Optical Gravitational Lensing Experiment Data
• Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
• Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars