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A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery
Wang, Ke1; Guo, Ping1,2; Luo, A-Li3
2017-03-01
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume465Issue:4Pages:4311-4324
AbstractSpectral feature extraction is a crucial procedure in automated spectral analysis. This procedure starts from the spectral data and produces informative and non-redundant features, facilitating the subsequent automated processing and analysis with machine-learning and data-mining techniques. In this paper, we present a new automated feature extraction method for astronomical spectra, with application in spectral classification and defective spectra recovery. The basic idea of our approach is to train a deep neural network to extract features of spectra with different levels of abstraction in different layers. The deep neural network is trained with a fast layer-wise learning algorithm in an analytical way without any iterative optimization procedure. We evaluate the performance of the proposed scheme on real-world spectral data. The results demonstrate that our method is superior regarding its comprehensive performance, and the computational cost is significantly lower than that for other methods. The proposed method can be regarded as a new valid alternative general-purpose feature extraction method for various tasks in spectral data analysis.
SubtypeArticle
KeywordMethods: Data Analysis Methods: Numerical Methods: Statistical Techniques: Spectroscopic
WOS HeadingsScience & Technology ; Physical Sciences
Funding OrganizationNational Natural Science Foundation of China(61375045) ; National Natural Science Foundation of China(61375045) ; Beijing Natural Science Foundation(4142030) ; Beijing Natural Science Foundation(4142030) ; National Natural Science Foundation of China (NSFC)(U1531242) ; National Natural Science Foundation of China (NSFC)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242) ; National Natural Science Foundation of China(61375045) ; National Natural Science Foundation of China(61375045) ; Beijing Natural Science Foundation(4142030) ; Beijing Natural Science Foundation(4142030) ; National Natural Science Foundation of China (NSFC)(U1531242) ; National Natural Science Foundation of China (NSFC)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242)
DOI10.1093/mnras/stw2894
WOS KeywordDEEP NEURAL-NETWORKS ; DIGITAL SKY SURVEY ; STELLAR SPECTRA ; 2-DIMENSIONAL CLASSIFICATION ; TELESCOPE LAMOST ; GALAXY ; DIMENSIONALITY ; ASTRONOMY
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61375045) ; National Natural Science Foundation of China(61375045) ; Beijing Natural Science Foundation(4142030) ; Beijing Natural Science Foundation(4142030) ; National Natural Science Foundation of China (NSFC)(U1531242) ; National Natural Science Foundation of China (NSFC)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242) ; National Natural Science Foundation of China(61375045) ; National Natural Science Foundation of China(61375045) ; Beijing Natural Science Foundation(4142030) ; Beijing Natural Science Foundation(4142030) ; National Natural Science Foundation of China (NSFC)(U1531242) ; National Natural Science Foundation of China (NSFC)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242) ; Chinese Academy of Sciences (CAS)(U1531242)
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000395170200040
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/8265
Collection光学天文研究部
Affiliation1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
2.Beijing Normal Univ, Image Proc & Pattern Recognit Lab, Beijing 100875, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Key Lab Optic Astron, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Wang, Ke,Guo, Ping,Luo, A-Li. A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2017,465(4):4311-4324.
APA Wang, Ke,Guo, Ping,&Luo, A-Li.(2017).A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,465(4),4311-4324.
MLA Wang, Ke,et al."A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 465.4(2017):4311-4324.
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