Spectra Classification Based on Local Mean-Based K-Nearest Centroid Neighbor Method
TU Liang-ping1, 3, WEI Hui-ming1, WANG Zhi-heng2*, WEI Peng3，LUO A-li3, ZHAO Yong-heng3
1. School of Science, University of Science and Technology Liaoning, Anshan 114051, China 2. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China 3. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
Abstract：In the present paper, a local mean-based K-nearest centroid neighbor (LMKNCN) technique is used for the classification of stars, galaxies and quasars(QSOS). The main idea of LMKNCN is that it depends on the principle of the nearest centroid neighborhood(NCN), and selects K centroid neighbors of each class as training samples and then classifies a query pattern into the class with the distance of the local centroid mean vector to the samples . In this paper, KNN, KNCN and LMKNCN were experimentally compared with these three different kinds of spectra data which are from the United States SDSS-DR8. Among these three methods, the rate of correct classification of the LMKNCN algorithm is higher than the other two algorithms or comparable and the average rate of correct classification is higher than the other two algorithms, especially for the identification of quasars. Experiment shows that the results in this work have important significance for studying galaxies, stars and quasars spectra classification.
