云南民族大学学报（自然科学版）2017，Vol.26Issue(2)：162-166,5.DOI:12.3969/j.issn.1672-8513.2017.02.015
基于深度信念网络的天体光谱自动分类研究
Automatic classification of star spectra based on the deep belief network
摘要
Abstract
This paper applies the deep belief network to the classification of astronomical spectra.First of all,the wavelet transform is used for the preliminary denoising of the spectral data.Then,the Principal Component Analysis (PCA) is used for the dimensionality reduction of the feature-value acquisition of the spectral data.Finally,this classifier is used for the study of some Cataclysmic Variable Stars in the Sloan Digital Sky Survey and then gives it a comparative study with the Restricted Boltzmann Machines (RBM).Because the deep belief network has data -based deep learning skills,it has the advantage of classifying the astronomical spectra,which has been proved in this study.关键词
光谱自动分类/深度信念网络/受限玻尔兹曼机/PCAKey words
automatic classification/deep belief network/Restricted Boltzmann Machines/PCA分类
信息技术与安全科学引用本文复制引用
刘真祥,荣容,许婷婷,周卫红..基于深度信念网络的天体光谱自动分类研究[J].云南民族大学学报（自然科学版）,2017,26(2):162-166,5.基金项目
国家自然科学基金(61561053) （61561053）
中国科学院天体结构与演化重点实验室(OP201512). （OP201512）
