New Approaches To Photometric Redshift Prediction
Expanding upon the work of Way & Srivastava (2006) we demonstrate how the use of training sets of comparable size continue to make Gaussian Process Regression a competitive approach to that of Neural Networks and other least squares fitting methods. This is possible via new large size matrix inversion techniques developed for Gaussian Processes that do not require that the kernel matrix be sparse. This development, combined with a neural-network kernel function appears to give superior results for this problem. Our best t results for the Sloan Digital Sky Survey Main Galaxy Sample using u,g,r,i,z gives an rms error of 0.0201 while our results for the same in the Luminous Red Galaxy Sample yield 0.0220.
Complete Metadata
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| description | Expanding upon the work of Way & Srivastava (2006) we demonstrate how the use of training sets of comparable size continue to make Gaussian Process Regression a competitive approach to that of Neural Networks and other least squares fitting methods. This is possible via new large size matrix inversion techniques developed for Gaussian Processes that do not require that the kernel matrix be sparse. This development, combined with a neural-network kernel function appears to give superior results for this problem. Our best t results for the Sloan Digital Sky Survey Main Galaxy Sample using u,g,r,i,z gives an rms error of 0.0201 while our results for the same in the Luminous Red Galaxy Sample yield 0.0220. |
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| modified | 2025-04-01 |
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| title | New Approaches To Photometric Redshift Prediction |
