DoPS: A Double-Peaked Profiles Search Method Based on the RS and SVM

Abstract

The double-peaked profiles in spectral data are very rare and valuable for the astronomers. Their recognitions are largely depended on visually inspect. The main reasons for auto-search of such spectra are the complex and distinct characteristics of astronomical data. In this paper, we address the problems by a double-peaked profiles search algorithm (called DoPS) based on relevant subspace (RS) and support vector machine (SVM). First, characteristics subspace is extracted by using the relevant subspace mining algorithm, in which the local density factor $lambda $ is particularly defined to measure the data sparsity. The characteristics of double-peaked profiles are represented by using the locations, the interval spaces, and strength ratio of double peaks. Second, the characteristics set is analyzed and grouped into three subsets according to the correlations among the characteristics based on the frequent patterns and rough set theory. Third, the double-peaked profiles search algorithm is proposed by using the support vectors trained from the labeled samples as thresholds. Finally, several spectral data sets from the LAMOST survey are employed to test the DoPS. The experimental results indicate that DoPS presents high performance than other similar algorithms in terms of time efficiency, noise immunity and recall, and reduced rates.