Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles. (arXiv:1902.07215v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Gilda_S/0/1/0/all/0/1">Sankalp Gilda</a>
An ever-looming threat to astronomical applications of machine learning is
the danger of over-fitting data, also known as the `curse of dimensionality.’
This occurs when there are fewer samples than the number of independent
variables. In this work, we focus on the problem of stellar parameterization
from low-mid resolution spectra, with blended absorption lines. We address this
problem using an iterative algorithm to sequentially prune redundant features
from synthetic PHOENIX spectra, and arrive at an optimal set of wavelengths
with the strongest correlation with each of the output variables — T$_{rm
eff}$, $log g$, and [Fe/H]. We find that at any given resolution, most
features (i.e., absorption lines) are not only redundant, but actually act as
noise and decrease the accuracy of parameter retrieval.
An ever-looming threat to astronomical applications of machine learning is
the danger of over-fitting data, also known as the `curse of dimensionality.’
This occurs when there are fewer samples than the number of independent
variables. In this work, we focus on the problem of stellar parameterization
from low-mid resolution spectra, with blended absorption lines. We address this
problem using an iterative algorithm to sequentially prune redundant features
from synthetic PHOENIX spectra, and arrive at an optimal set of wavelengths
with the strongest correlation with each of the output variables — T$_{rm
eff}$, $log g$, and [Fe/H]. We find that at any given resolution, most
features (i.e., absorption lines) are not only redundant, but actually act as
noise and decrease the accuracy of parameter retrieval.
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