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Improving Accuracy of Quasars' Photometric Redshift Estimation by Integration of KNN and SVM

Published online by Cambridge University Press:  27 October 2016

Bo Han ,
Hongpeng Ding ,
Yanxia Zhang  and
Yongheng Zhao
Bo Han
Affiliation:
International School of Software, Wuhan University, Wuhan, P.R.China email: [email protected]; [email protected]
Hongpeng Ding
Affiliation:
International School of Software, Wuhan University, Wuhan, P.R.China email: [email protected]; [email protected]
Yanxia Zhang
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, 100012, Beijing, P.R.China
Yongheng Zhao
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, 100012, Beijing, P.R.China
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Abstract

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Catastrophic failure is an unsolved problem existing in the most photometric redshift estimation approaches for a long history. In this study, we propose a novel approach by integration of k-nearest-neighbor (KNN) and support vector machine (SVM) methods together. Experiments based on the quasar sample from SDSS show that the fusion approach can significantly mitigate catastrophic failure and improve the accuracy of photometric redshift estimation.

Keywords

Photometric redshiftsK-Nearest-NeighbourSupport Vector MachineSDSS
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , General Assembly A29A: Astronomy in Focus , August 2015 , pp. 209
Copyright
Copyright © International Astronomical Union 2016