With the increase of stellar spectra, how to automatically classify these spectra have attracted astronomer's attention. Support Vector Machine (SVM), as a typical classifier, has widely used in stellar spectra classification. Due to its limited performance in various classification problems and higher training time, a model with a pair of hyperspheres named Twin Hypersphere Model (THM), proposed by Peng and Xu, is utilized for stellar spectra classification in this paper. In THM, the samples in one hypersphere is far from another according to the Euclidean distance. The comparative experiments with SVM and Twin Support Vector Machine (TWSVM) on the SDSS datasets shows that the THM model gives the best classification accuracy of 0.8836 for type F, 0.9446 for type G, and 0.9509 for type K, which are better than the classification accuracies of 0.8000, 0.8484, 0.8911 obtained by SVM and 0.8413, 0.8699, 0.9109 obtained by TWSVM. It can be concluded that THM perform better than traditional techniques such as SVM and TWSVM on the K-, F-, G- type stellar spectra classification.

随着恒星光谱的增加，如何自动对这些光谱进行分类吸引了天文学家的注意力。支持向量机（SVM）作为典型的分类器，已广泛用于恒星光谱分类。由于其在各种分类问题和较高的训练时间方面的性能有限，本文将由Peng和Xu提出的带有一对超球的模型称为Twin Hypersphere Model（THM）用于恒星光谱分类。在THM中，根据欧几里得距离，一个超球面中的样本与另一个超球面相距较远。在SDSS数据集上使用SVM和双支持向量机（TWSVM）进行的比较实验表明，THM模型对F型的最佳分类精度为0.8836，对于G型的分类精度为0.9446，对于K型的分类精度为0.9509，分别优于SVM获得的0.8000、0.8484、0.8911和TWSVM获得的0.8413、0.8699、0.9109的分类精度。可以得出结论，在K型，F型，G型恒星光谱分类上，THM的性能优于SVM和TWSVM等传统技术。