Stellar Spectra Classification Method Based on Multi-Class Support Vector Machine

作者：Zhang Jing; Liu Zhong-bao^*; Song Wen-ai; Fu Li-zhen; Zhang Yong-lai

来源：Spectroscopy and Spectral Analysis, 2018, 38(7): 2307-2310.

DOI：10.3964/j.issn.1000-0593(2018)07-2307-04

• Support vector machine
• Multi-class support vector machine
• Stellar spectra
• Automatic classification

Support vector machine (SVM), a typical classification method, has been widely used in stellar spectra classification. It performs well in practice, while it encounters the multi-class classification challenge. In order to solve the problem above, multi-class support vector machine (MCSVM) was proposed in this paper based on the in-depth analysis of SVM. Meanwhile, the stellar spectra classification model based on multi-class support vector machine was constructed. The advantage of the proposed method is that the samples' class can be determined by a classification process. Comparative experiments with the existed multi-class classification method on the SDSS DR8 datasets verify the effectiveness of the proposed method.

• 出版日期2018-7
• 单位中北大学