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PKDD
2009
Springer

Kernels for Periodic Time Series Arising in Astronomy

9 years 2 months ago
Kernels for Periodic Time Series Arising in Astronomy
Abstract. We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently such classification requires a large amount of domain expert time. We show that a combination of phase invariant similarity and explicit features extracted from the time series provide domain expert level classification. To facilitate this application, we investigate the cross-correlation as a general phase invariant similarity function for time series. We establish several theoretical properties of cross-correlation showing that it is intuitively appealing and algorithmically tractable, but not positive semidefinite, and therefore not generally applicable with kernel methods. As a solution we introduce a positive semidefinite similarity function with the same intuitive appeal as cross-correlation. An experimental evaluation in the astronomy domain as well as several other data sets demonstrates the perform...
Gabriel Wachman, Roni Khardon, Pavlos Protopapas,
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PKDD
Authors Gabriel Wachman, Roni Khardon, Pavlos Protopapas, Charles R. Alcock
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