http://arxiv.org/abs/1911.05821
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.
K. Johnston, S. Caballero-Nieves, V. Petit, et. al.
Fri, 15 Nov 19
58/73
Comments: 16 pages, 11 figures
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