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.Comment: Proceedings of ADASS17 (September 2007, London). 4 pages. To appear
in ASP Conf. Pro