提出一套适用于在海量光谱中快速发现激变变星的方法。 针对SDSS发布的DR8数据, 尝试流型学习方法在海量光谱数据挖掘中的应用。 首先使用非线性局部线性嵌入方法(LLE)对海量光谱数据进行降维, 然后使用人工神经网络对低维数据进行分类, 最后对较少数量的候选体进行人工证认。 实验共发现了6个新的激变变星候选体, 并与传统的PCA方法进行了比较, 验证了LLE方法在天文数据挖掘中的可行性。

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.