title: [Data mining for cataclysmic variables candidates in SDSS-DR8].
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.
authors.affiliation: School of Mechanical, Electrical & Information Engineering, Shandong University at Weihai, Weihai 264209, China. jiangbin@sdu.edu.cn
authors.forename: Bin
authors.initials: B
authors.lastname: Jiang
authors.forename: Jing-Chang
authors.initials: JC
authors.lastname: Pan
authors.forename: Wei
authors.initials: W
authors.lastname: Wang