Stellar Spectra Classification by Support Vector Machine with Spectral Distribution Properties
LIU Zhong-bao1, 2, QIN Zhen-tao1, LUO Xue-gang1, ZHOU Fang-xiao1, ZHANG Jing1
1. School of Mathematics and Computer Science, Panzhihua University, Panzhihua 617000, China
2. School of Software, North University of China, Taiyuan 030051, China
Abstract：Stellar spectra classification is one of hot spots in astronomy. With hundreds and thousands of spectra obtained by researchers, it is a big challenge to process them manually. It’s urgent to apply the automatic technologies, especially the data mining algorithms, to classify the stellar spectra. Neural networks, self organization mapping, association rules and other data mining algorithms have been utilized to classify the stellar spectra in recent years. In these methods, Support Vector Machine (SVM), as a typical classification method, is widely used in the stellar spectra classification due to its good learning capability and excellent classification performance. The basic idea of standard SVM is to find an optimal separating hyper-plane between the positive and negative samples. Its time complexity is so high that its classification efficiencies can’t be greatly improved. Twin Support Vector Machine (TWSVM) is proposed to deal with the above problem. It aims at generating two non-parallel hyper-planes such that each plane is close to one class and as far as possible from the other one. The learning speed of TWSVM is approximately four times faster than the classical SVM. The limitation of TWSVM is that it doesn’t take spectral distribution properties into consideration, and its efficiencies are prone to be influenced by noise and singular points. In view of this, Fuzzy Twin Support Vector Machine with Spectral Distribution Properties (TWSVM-SDP) is proposed, in which between-class scatter and within-class scatter in Linear Discriminant Analysis (LDA) is introduced to describe the spectral distribution properties and the fuzzy membership function is introduced to decrease the influences of noise and singular points. Comparative experiments on SDSS DR8 stellar spectra datasets verity TWSVM-SDP performs better than SVM and TWSVM. However, some limitations exist in TWSVM-SDP, for example, how to deal with the mass spectra is quite difficult to solve. We will research the adaptability of our proposed method in the big data environment based on big data technologies.
Key words：Stellar spectra; Classification; Spectral distribution properties; Fuzzy membership function; Twin support vector machine
刘忠宝，秦振涛，罗学刚，周方晓，张 靖. 利用融合数据分布特征的模糊双支持向量机对恒星光谱分类[J]. 光谱学与光谱分析, 2019, 39(04): 1307-1311.
LIU Zhong-bao, QIN Zhen-tao, LUO Xue-gang, ZHOU Fang-xiao, ZHANG Jing. Stellar Spectra Classification by Support Vector Machine with Spectral Distribution Properties. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1307-1311.
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