Unsupervised classification of Galaxies. I. independent component analysis feature selection

Abstract

Subjective classification of galaxies can mislead us in the quest for the formation and evolution of galaxies since this is necessarily limited to a few features. The human mind is unable to comprehend the complex correlations in a manifold parameter space, and multivariate analyses are the best tools for understanding the differences among various kinds of objects. In this series of papers, an objective classification of 362,923 galaxies from the Value Added Galaxy Catalog is carried out with the help of two methods of multivariate analysis. First, Independent Component Analysis is used to determine a set of derived independent components that are linear combinations of 47 observed features (namely, ionized lines, Lick indices, photometric and morphological properties, star formation rates, etc.) of the galaxies. Subsequently, a K-means cluster analysis is applied to the nine independent components to obtain 10 distinct, homogeneous groups. In this first paper, we describe the methods and the main results. It appears that the nine Independent Components represent a complete physical description of galaxies (velocity dispersion, ionization, metallicity, surface brightness, and structure). We find that our 10 groups can be essentially placed into traditional and empirical classes (from color–magnitude and emission-line diagnostic diagrams, early versus late types) despite the classical corresponding features (color, line ratios, and morphology) not being significantly correlated with the nine Independent Components. A more detailed physical interpretation of the groups will be performed in subsequent papers.