Automated Classification for Celestial Spectra Based on Cover Algorithm
YANG JinFu1, WU FuChao1, LUO ALi2, ZHAO YongHeng2
1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080 2.National Astronomical Observatory, Chinese Academy of Sciences, Beijing 100012
Abstract Automated classification for large numbers of celestial spectra is one of the most important problems which are urgent to be solved in large survey projects. In this paper, an automated classification method for celestial spectra based on cover algorithm is presented. In the training procedure, some representative spectra of the training spectrum set can be obtained by utilizing a certain cover rule. Then, in the classification phase, the classifier computes the distances between each test spectrum and the representatives, and the class of the test spectrum is determined by the representative spectrum closest to the test spectrum. The experimental results show that this method is better than SVM both in training speed and classifying accuracy over Normal Galaxies(NGs), Stars, Active Galaxies(AGs) and Active Galactic Nucleus(AGNs) datasets. And the proposed method is promising for the automated classification of large numbers of celestial spectra collected by large survey projects.
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2006, Vol. 19 
