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Automated classification of celestial spectra based on support vector machines
Tan DM(覃冬梅)1; Hu ZY(胡占义)1; Zhao YH(赵永恒)2
AbstractThe main objective of an automatic recognition system of celestial objects via their spectra is to classify celestial spectra and estimate physical parameters automatically. This paper proposes a new automatic classification method based on support vector machines to separate non-active objects from active objects via their spectra. With low SNR and unknown red-shift value, it is difficult to extract true spectral lines, and as a result, active objects can not be determined by finding strong spectral lines and the spectral classification between non-active and active objects becomes difficult. The proposed method in this paper combines the principal component analysis with support vector machines, and can automatically recognize the spectra of active objects with unknown red-shift values from non-active objects. It finds its applicability in the automatic processing of voluminous observed data from large sky surveys in astronomy.
KeywordSTELLAR SPECTRA GALAXIES LIBRARY support vector machines principal component analysis active objects non-active objects automated spectral classification
Document Type期刊论文
Recommended Citation
GB/T 7714
Tan DM,Hu ZY,Zhao YH. Automated classification of celestial spectra based on support vector machines[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2004,24(4):507.
APA 覃冬梅,胡占义,&赵永恒.(2004).Automated classification of celestial spectra based on support vector machines.SPECTROSCOPY AND SPECTRAL ANALYSIS,24(4),507.
MLA 覃冬梅,et al."Automated classification of celestial spectra based on support vector machines".SPECTROSCOPY AND SPECTRAL ANALYSIS 24.4(2004):507.
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