Exploration of Approach to Mining WDMS Spectra based on Laplacian Eigenmap and Neural Network

For the purpose of discovering White Dwarf + Main Sequence (WDMS) from massive spectra, in this paper, an unsupervised learning algorithm for Nonlinear Dimensionality Reduction named Laplacian Eigenmap is discussed. It turns out that, comparing with Principle Component Analysis (PCA), Laplacian Eigenmap maintains the information of nonlinear structure of high dimensional spectral data, which leads to a higher classification accuracy. In the feature space, backpropagation neural network is used to classify WDMS and non-WDMS spectra. Furthermore, Particle Swarm Optimization (PSO) is implemented to increase the classification accuracy via optimizing the parameters of the network. The results shows that the method in this paper can discover WDMS efficiently and accurately after training the neural network with low-dimensional data from Sloan Digital Sky Survey Data Release 10 (SDSS-DR10).