[Automated classification of celestial spectra based on support vector machines].
The 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.
Qin
Dong-mei
DM
National Pattern Recognition Laboratory of Automation Institute, Chinese Academy of Sciences, Beijing 100080, China.
Hu
Zhan-yi
ZY
Zhao
Yong-heng
YH
D000465:Algorithms; D001185:Artificial Intelligence; D001256:Astronomy; D019295:Computational Biology; D001330:Electronic Data Processing; D016247:Information Storage and Retrieval; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted