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.
@article{arxiv.1911.05821,
title = {Variable Star Classification Using Multi-View Metric Learning},
author = {K. B. Johnston and S. M. Caballero-Nieves and V. Petit and A. M. Peter and R. Haber},
journal= {arXiv preprint arXiv:1911.05821},
year = {2020}
}