ELM-KNN for photometric redshift estimation of quasars. Issue Volume 12:Issue S325(2016) (30th May 2017)

• Record Type: Journal Article
• Authors: Zhang, Yanxia; Tu, Yang; Zhao, Yongheng; Tian, Haijun
• Editors: Brescia, M.; Djorgovski, S.G.; Feigelson, E.; Longo, G.; Cavuoti, S.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 12:Issue S325(2016)
• Page Start: 225
• Page End: 228
• Publication Date: 2017-05-30
• Subjects: methods: data analysis, -- techniques: photometric, -- quasars: general, -- catalogs, -- surveys; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921316012679
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: 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 ( k NN) 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 k NN. The experimental results show that ELM-KNN is feasible and superior to k NN (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.