- Classification of large-scale stellar spectra based on the non-linearly assembling learning machine
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- 发表刊物：MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
- 关键字：methods; data analysis - methods; statistical - techniques; spectroscopic - astronomical data bases; miscellaneous - stars; fundamental parameters - stars; statistics
- 摘要：An important problem to be solved of traditional classification methods is they cannot deal with large-scale classification because of very high time complexity. In order to solve above problem, inspired by the thinking of collaborative management, the non-linearly assembling learning machine (NALM) is proposed and used in the large-scale stellar spectral classification. In NALM, the large-scale dataset is firstly divided into several subsets, and then the traditional classifiers such as support vector machine (SVM) runs on the subset, finally, the classification results on each subset are assembled and the overall classification decision is obtained. In comparative experiments, we investigate the performance of NALM in the stellar spectral subclasses classification compared with SVM. We apply SVM and NALM respectively to classify the four subclasses of K-type spectra, three subclasses of F-type spectra and three subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS). The comparative experiment results show that the performance of NALM is much better than SVM in view of the classification accuracy and the computation time.
- 全部作者： Zhao Wenjuan,Song Lipeng
- 通讯作者：Liu Zhongbao
- 卷号：455
- 期号：4
- 页面范围：4289-4294
- ISSN号：0035-8711
- 是否译文：否
- 发表时间：2016-02-01
