| 光谱学与光谱分析 |
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| Automatic Classification Method of Star Spectra Data Based on Manifold Fuzzy Twin Support Vector Machine |
| LIU Zhong-bao1, GAO Yan-yun2, WANG Jian-zhen3 |
1. School of Electronics and Computer Science Technology, North University of China, Taiyuan 030051, China
2. Human Resource Department, Henan Information and Statistics Vocational College, Zhengzhou 450008, China
3. School of Information, Business College of Shanxi University, Taiyuan 030031, China |
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Abstract Support vector machine (SVM) with good leaning ability and generalization is widely used in the star spectra data classification. But when the scale of data becomes larger, the shortages of SVM appear: the calculation amount is quite large and the classification speed is too slow. In order to solve the above problems, twin support vector machine (TWSVM) was proposed by Jayadeva. The advantage of TSVM is that the time cost is reduced to 1/4 of that of SVM. While all the methods mentioned above only focus on the global characteristics and neglect the local characteristics. In view of this, an automatic classification method of star spectra data based on manifold fuzzy twin support vector machine (MF-TSVM) is proposed in this paper. In MF-TSVM, manifold-based discriminant analysis (MDA) is used to obtain the global and local characteristics of the input data and the fuzzy membership is introduced to reduce the influences of noise and singular data on the classification results. Comparative experiments with current classification methods, such as C-SVM and KNN, on the SDSS star spectra datasets verify the effectiveness of the proposed method.
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Received: 2013-09-08
Accepted: 2013-12-22
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Corresponding Authors:
LIU Zhong-bao
E-mail: liu_zhongbao@hotmail.com
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