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Learning Vector Quantization for Classifying Astronomical
Zhang Yanxia; Zhao Yongheng
Source PublicationChinese Journal of Astronomy and Astrophysics
AbstractThe sizes of astronomical surveys in different wavebands are increas-ing rapidly. Therefore, automatic classification of objects is becoming ever moreimportant. We explore the performance of learning vector quantization (LVQ) inclassifying multi-wavelength data. Our analysis concentrates on separating activesources from non-active ones. Different classes of X-ray emitters populate distinctregions of a multidimensional parameter space. In order to explore the distributionof various objects in a multidimensional parameter space, we positionally cross-correlate the data of quasars, BL Lacs, active galaxies, stars and normal galaxiesin the optical, X-ray and infrared bands. We then apply LVQ to classify them withthe obtained data. Our results show that LVQ is an effective method for separatingAGNs from stars and normal galaxies with multi-wavelength data.
Keywordmethod: data analysis method: statistical catalogs
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang Yanxia,Zhao Yongheng. Learning Vector Quantization for Classifying Astronomical[J]. Chinese Journal of Astronomy and Astrophysics,2003,3(2):183.
APA Zhang Yanxia,&Zhao Yongheng.(2003).Learning Vector Quantization for Classifying Astronomical.Chinese Journal of Astronomy and Astrophysics,3(2),183.
MLA Zhang Yanxia,et al."Learning Vector Quantization for Classifying Astronomical".Chinese Journal of Astronomy and Astrophysics 3.2(2003):183.
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