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Featureless classification of light curves
2015
Monthly notices of the Royal Astronomical Society
Classification of irregularly sampled time series is extremely difficult because the data cannot be represented naturally as a vector directly useable by a classifier. While in the literature, various statistical features serve as vector representations, time series are represented by a density model in this work. The density model can capture all information available in the static behavior of a light curve and including measurement errors properly. Subsequently, a distance matrix is created,
doi:10.1093/mnras/stv1181
fatcat:2hurgqkajbcizlg4pr6dx35gde