Name
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 performance of the kernel and related similarity functions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04174-7_32 | ECML/PKDD |
Keywords | DocType | Volume |
domain expert level classification,time series,general phase invariant similarity,phase invariant similarity,periodic time series arising,cross-correlation showing,related similarity function,domain expert time,astronomy domain,positive semidefinite similarity function,automatic classification,feature extraction,kernel method,machine learning,cross correlation | Conference | 5782 |
ISSN | Citations | PageRank |
0302-9743 | 10 | 0.75 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Wachman | 1 | 95 | 5.42 |
| Roni Khardon | 2 | 1068 | 133.16 |
| Pavlos Protopapas | 3 | 127 | 14.73 |
| Charles R. Alcock | 4 | 10 | 0.75 |