| 光谱学与光谱分析 |
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| Data Mining for Cataclysmic Variables Candidates in SDSS-DR8 |
| JIANG Bin, PAN Jing-chang, WANG Wei |
| School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China |
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Abstract An automatic and efficient method for cataclysmic variables candidates is presented in this paper. The nonlinear locally linear embedding-LLE method is applied in the newly released SDSS-DR8 spectra. Spectra are dimension-reduced by LLE and classified by artificial neural network. The greatly reduced final candidates can be identified manually. 6 new CVs candidates were found in the experiment, and the compare between LLE with PCA shows the feasibility of nonlinear method in data mining in astronomical data.
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Received: 2012-06-25
Accepted: 2012-09-29
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Corresponding Authors:
JIANG Bin
E-mail: jiangbin@sdu.edu.cn
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