光谱学与光谱分析 |
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A New Method of Sparse Feature Extraction for Stellar Spectra |
LU Yu, LI Xiang-ru*, YANG Tan, WANG Yong-jun |
School of Mathematical Sciences, South China Normal University, Guangzhou 510631, China |
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Abstract The authors propose a novel method of feature extraction for stellar spectra parameterization. The basic procedures are: First, stellar spectra are decomposed by multi-scale Harr wavelet and the coefficients with high-frequency are rejected. Secondly, the optimal features are detected by the lasso algorithm. Finally, we input the optimal feature vector to non-parametric regression model to estimate the atmospheric parameters. Haar wavelet can remove the high-frequency noise from the stellar spectrum. Lasso algorithm can further compress data by analyzing their significance on parameterization and removing redundancy. Experiments show that the proposed Haar+lasso method improves the accuracy and efficiency of the estimation. The authors used this scheme to estimate the atmospheric parameters from a subsample of some 40 000 stellar spectra from SDSS. The accuracies of our predictions (mean absolute errors) for each parameter are 0.007 1 dex for log Teff, 0.225 2 dex for log g, and 0.199 6 dex for [Fe/H]. Compared with the results of the existing literature, this scheme can derive more accurate atmospheric parameters.
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Received: 2013-09-22
Accepted: 2013-12-08
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Corresponding Authors:
LI Xiang-ru
E-mail: xiangru.li@gmail.com
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