title: [A new method of sparse feature extraction for stellar spectra].
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 0.0071 dex for log Teff, 0.2252 dex for log g, and 0.1996 dex for [Fe/H]. Compared with the results of the existing literature, this scheme can derive more accurate atmospheric parameters.
authors.forename: Yu
authors.initials: Y
authors.lastname: Lu
authors.forename: Xiang-ru
authors.initials: XR
authors.lastname: Li
authors.forename: Tan
authors.initials: T
authors.lastname: Yang
authors.forename: Yong-jun
authors.initials: YJ
authors.lastname: Wang