• Stellar Spectral Classification with Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine

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    • Keywords


      Stellar spectral classification; support vector machine (SVM); Fisher’s discriminant analysis (FDA); class distribution.

    • Abstract


      Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM-named Minimum within-class and Maximum between-class scatter Support Vector Machine (MMSVM) is constructed to deal with the above problem. MMSVM merges the advantages of Fisher’s Discriminant Analysis (FDA) and SVM, and the comparative experiments on the Sloan Digital Sky Survey (SDSS) show that MMSVM performs better than SVM.

    • Author Affiliations


      Liu Zhong-Bao1

      1. School of Computer and Control Engineering, North University of China, Taiyuan 030051, China.
    • Dates

  • Journal of Astrophysics and Astronomy | News

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