Astronomy and Astrophysics – Astronomy
Scientific paper
Jul 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010spie.7740e..88w&link_type=abstract
Software and Cyberinfrastructure for Astronomy. Edited by Radziwill, Nicole M.; Bridger, Alan. Proceedings of the SPIE, Volume 7
Astronomy and Astrophysics
Astronomy
Scientific paper
We investigate two methods: kernel regression and nearest neighbor algorithm for photometric redshift estimation with the quasar samples from SDSS (the Sloan Digital Sky Survey) and UKIDSS (the UKIRT Infrared Deep Sky Survey) databases. Both kernel regression and nearest neighbor algorithm belong to the family of instance-based learning algorithms, which store all the training examples and "delay learning" until prediction time. The major difference between the two algorithms is that kernel regression is a weighted average of spectral redshifts of the neighbors for a query point while nearest neighbor algorithm utilizes the spectral redshift of the nearest neighbor for a query point. Each algorithm has its own advantage and disadvantage. Our experimental results show that kernel regression obtains more accurate predicting results, and nearest neighbor algorithm shows its superiority especially for more thinly spread data, e.g. high redshift quasars.
Wang Dan
Zhang Yan-Xia
Zhao Yong-Heng
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