Abstract
Nearest neighbor models are among the most basic tools in machine learning, and recent work has demonstrated their effectiveness in the field of astronomy. The performance of these models crucially depends on the underlying metric, and in particular on the selection of a meaningful subset of informative features. The feature selection is task-dependent and usually very time-consuming. In this work, we propose an efficient parallel implementation of incremental feature selection for nearest neighbor models utilizing nowadays graphics processing units. Our framework provides significant computational speed-ups over its sequential single-core competitor of up to two orders of magnitude. We demonstrate the applicability of the overall scheme on one of the most challenging tasks in astronomy: redshift estimation for distant galaxies.
| Original language | English |
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| Title of host publication | ESANN 2014 proceedings : European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Editors | M. Verleysen |
| Number of pages | 6 |
| Publisher | i6doc.com |
| Publication date | 2014 |
| Pages | 87-92 |
| ISBN (Print) | 978-287419095-7 |
| Publication status | Published - 2014 |
| Event | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2014 - Bruges, Belgium Duration: 23 Apr 2014 → 25 Apr 2014 |
Conference
| Conference | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2014 |
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| Country/Territory | Belgium |
| City | Bruges |
| Period | 23/04/2014 → 25/04/2014 |