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ELM-KNN for photometric redshift estimation of quasars

Published online by Cambridge University Press:  30 May 2017

Yanxia Zhang ,
Yang Tu ,
Yongheng Zhao  and
Haijun Tian
Yanxia Zhang
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, P.R.China email: [email protected]
Yang Tu
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, P.R.China email: [email protected] College of Science, China Three Gorges University, Yichang, Hubei, P.R.China
Yongheng Zhao
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, P.R.China email: [email protected]
Haijun Tian
Affiliation:
College of Science, China Three Gorges University, Yichang, Hubei, P.R.China
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Abstract

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We explore photometric redshift estimation of quasars with the SDSS DR12 quasar sample. Firstly the quasar sample is separated into three parts according to different redshift ranges. Then three classifiers based on Extreme Learning Machine (ELM) are created in the three redshift ranges. Finally k-Nearest Neighbor (kNN) approach is applied on the three samples to predict photometric redshifts of quasars with multiwavelength photometric data. We compare the performance with different input patterns by ELM-KNN with that only by kNN. The experimental results show that ELM-KNN is feasible and superior to kNN (e.g. rms is 0.0751 vs. 0.2626 for SDSS sample), in other words, the ensemble method has the potential to increase regressor performance beyond the level reached by an individual regressor alone and will be a good choice when facing much more complex data.

Keywords

methods: data analysistechniques: photometricquasars: generalcatalogssurveys
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 225 - 228
Copyright
Copyright © International Astronomical Union 2017 

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