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| Paper: |
Improving the Performance of Photometric Regression Models via Massive Parallel Feature Selection |
| Volume: |
485, Astronomical Data Analysis Software and Systems XXIII |
| Page: |
425 |
| Authors: |
Polsterer, K. L.; Gieseke, F.; Igel, C.; Goto, T. |
| Abstract: |
Regression tasks are common in astronomy, for instance, the estimation
of the redshift or the metallicity of galaxies. Generating regression
models, however, is often hindered by the heterogeneity of the available
input catalogs, which leads to missing data and/or features of differing
explanatory power. In this work, we show how simple but effective
feature selection schemes from data mining can be used to significantly
improve the performance of regression models for photometric redshift
and metallicity estimation (even without any particular knowledge of the
input parameters' physical properties). Our framework tests huge amounts
of possible feature combinations. Since corresponding (single-core)
implementations are computationally very demanding, we make use of the
massive computational resources provided by nowadays graphics processing
units to significantly reduce the overall runtime. This renders an
exhaustive search possible, as we demonstrate in our experimental
evaluation. We conclude the work by discussing further applications of
our approach in the context of large-scale astronomical learning
settings. |
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