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Astrophysics

arXiv:0711.4465 (astro-ph)
[Submitted on 28 Nov 2007]

Title:A method for exploiting domain information in astrophysical parameter estimation

Authors:C.A.L. Bailer-Jones (Max Planck Institute for Astronomy, Heidelberg)
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Abstract: I outline a method for estimating astrophysical parameters (APs) from multidimensional data. It is a supervised method based on matching observed data (e.g. a spectrum) to a grid of pre-labelled templates. However, unlike standard machine learning methods such as ANNs, SVMs or k-nn, this algorithm explicitly uses domain information to better weight each data dimension in the estimation. Specifically, it uses the sensitivity of each measured variable to each AP to perform a local, iterative interpolation of the grid. It avoids both the non-uniqueness problem of global regression as well as the grid resolution limitation of nearest neighbours.
Comments: Proceedings of ADASS17 (September 2007, London). 4 pages. To appear in ASP Conf. Proc
Subjects: Astrophysics (astro-ph)
Cite as: arXiv:0711.4465 [astro-ph]
  (or arXiv:0711.4465v1 [astro-ph] for this version)
  https://doi.org/10.48550/arXiv.0711.4465

Submission history

From: Coryn Bailer-Jones [view email]
[v1] Wed, 28 Nov 2007 10:36:36 UTC (267 KB)
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