Classification of Quasars and Stars by Supervised and Unsupervised Methods

Abstract

Targeting quasar candidates is always an important task for large spectroscopic sky survey projects. Astronomers never give up thinking out effective approaches to separate quasars from stars. The previous methods on this issue almost belong to supervised methods or color-color cut. In this work, we compare the performance of a supervised method – Support Vector Machine (SVM)– with that of an unsupervised method one-class SVM. The performance of SVM is better than that of one-class SVM. But one-class SVM is an unsupervised algorithm which is helpful to recognize rare or mysterious objects. Combining supervised methods with unsupervised methods is effective to improve the performance of a single classifier.