Stellar spectra classification with twin hypersphere model |
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| Affiliation: | 1. Physics Engineering, Hacettepe University, TR-06800 Ankara, Turkey;2. Umeå University, SE-907 38 Umeå, Sweden;1. Department of Mathematics, Graphic Era Hill University, Dehradun, Uttarakhand, India 248002;2. Department of Mathematics, Graphic Era (deemed to be) University, Dehradun, Uttarakhand, India 248002;3. Indraprastha Institute of Management and Technology, Saharanpur, Uttar Pradesh, India 247551;4. Former Professor of Mathematics, Indian Institute of Technology, Roorkee, Uttarakhand, India 247671;5. Emeritus Professor of Mathematics, Ambala College of Engineering and Applied Research, Devasthali, Ambala Cantt, Haryana, India 133101;1. Observatorio Astronómico, Universidad Nacional de Córdoba, Laprida 854, Córdoba, X5000BGR, Argentina;2. Universidad Nacional Autónoma de México, Instituto de Astronomía, AP 70-264, CDMX 04510, México;3. Departamento de Astrofísica, Universidad de La Laguna, E-38206 La Laguna, Tenerife, Spain;4. Instituto de Astrofísica de Canarias (IAC), E-38205 La Laguna, Tenerife, Spain;5. Universidad Nacional Autónoma de México, Instituto de Astronomía, AP 70-264, CDMX 04510, México;6. Universidad de Guanajuato, Departamento de Astronomía, Guanajuato 36000, México;7. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina;1. Department of Physics and Institute of Astronomy, National Tsing Hua University, Hsin-Chu, Taiwan;2. Institute of Computational and Modeling Science, National Tsing Hua University, Hsin-Chu, Taiwan |
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| Abstract: | With the increase of stellar spectra, how to automatically classify these spectra have attracted astronomer's attention. Support Vector Machine (SVM), as a typical classifier, has widely used in stellar spectra classification. Due to its limited performance in various classification problems and higher training time, a model with a pair of hyperspheres named Twin Hypersphere Model (THM), proposed by Peng and Xu, is utilized for stellar spectra classification in this paper. In THM, the samples in one hypersphere is far from another according to the Euclidean distance. The comparative experiments with SVM and Twin Support Vector Machine (TWSVM) on the SDSS datasets shows that the THM model gives the best classification accuracy of 0.8836 for type F, 0.9446 for type G, and 0.9509 for type K, which are better than the classification accuracies of 0.8000, 0.8484, 0.8911 obtained by SVM and 0.8413, 0.8699, 0.9109 obtained by TWSVM. It can be concluded that THM perform better than traditional techniques such as SVM and TWSVM on the K-, F-, G- type stellar spectra classification. |
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