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

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• 作者：Liu Zhong-Bao
• 刊名：Journal of Astrophysics and Astronomy
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：37
• 期：2
• 全文大小：171 KB
• 参考文献：Almeida, J. S., Prieto, C. A. 2013, AJ, 763, 1.CrossRef
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• 作者单位：Liu Zhong-Bao (1)
  
  1. School of Computer and Control Engineering, North University of China, Taiyuan, 030051, China.
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Astronomy
  Astrophysics
  
• 出版者：Springer India
• ISSN：0973-7758

文摘

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.