Classification in Multidimensional Parameter Space: Methods and Examples

Abstract

ABSTRACTWe put forward two classification algorithms, support vector machines (SVM) and learning vector quantization (LVQ), to study the distribution of various astronomical sources in the multidimensional parameter space. By positional cross‐correlation, the multiwavelength data of 1656 active galactic nuclei (AGNs), 3718 stars, and 173 galaxies are obtained from optical (USNO‐A2.0), X‐ray (ROSAT), and infrared (Two Micron All‐Sky Survey) bands. We have applied principal component analysis (PCA) to the sample, unveiling correlations between parameters and reducing the dimensionality of the input parameter space. Then, taking the preprocessed data of PCA as input, we apply SVM and LVQ to classify stars, AGNs, and normal galaxies and compare their performances. From the classified results, we conclude that PCA+LVQ and PCA+SVM are effective methods to classify sources with multiwavelength data; moreover, the two methods gave comparable results in a number of situations. Generally, PCA+SVM gave better results; however, PCA+LVQ was considerably faster in terms of computation time. What is more, the classifiers derived by these methods can be used to preselect candidates for large surveys, reducing time and energy wasted. Therefore, the efficiency of high‐cost telescopes will be improved. In addition, these methods will be useful to develop the toolkits of the International Virtual Observatory.