JDAIP  Vol.3 No.3 , August 2015
Krylov Iterative Methods for Support Vector Machines to Classify Galaxy Morphologies
ABSTRACT
Large catalogues of classified galaxy images have been useful in many studies of the universe in astronomy. There are too many objects to classify manually in the Sloan Digital Sky Survey, one of the premier data sources in astronomy. Therefore, efficient machine learning and classification algorithms are required to automate the classifying process. We propose to apply the Support Vector Machine (SVM) algorithm to classify galaxy morphologies and Krylov iterative methods to improve runtime of the classification. The accuracy of the classification is measured on various categories of galaxies from the survey. A three-class algorithm is presented that makes use of multiple SVMs. This algorithm is used to assign the categories of spiral, elliptical, and irregular galaxies. A selection of Krylov iterative solvers are compared based on their efficiency and accuracy of the resulting classification. The experimental results demonstrate that runtime can be significantly improved by utilizing Krylov iterative methods without impacting classification accuracy. The generalized minimal residual method (GMRES) is shown to be the most efficient solver to classify galaxy morphologies.

Cite this paper
Freed, M. and Lee, J. (2015) Krylov Iterative Methods for Support Vector Machines to Classify Galaxy Morphologies. Journal of Data Analysis and Information Processing, 3, 72-86. doi: 10.4236/jdaip.2015.33009.
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