Unsupervised Classification of Galaxies. I. ICA feature selection
  Subjective classification of galaxies can mislead us in the quest of the
origin regarding formation and evolution of galaxies since this is necessarily
limited to a few features. The human mind is not able to apprehend the complex
correlations in a manyfold parameter space, and multivariate analyses are the
best tools to understand the differences among various kinds of objects. In
this series of papers, an objective classification of 362,923 galaxies from the
Value Added Galaxy Catalogue (VAGC) is carried out with the help of two methods
of multivariate analysis. First, Independent Component Analysis (ICA) is used
to determine a set of derived independent components that are linear
combinations of 47 observed features (viz. ionized lines, Lick indices,
photometric and morphological properties, star formation rates etc.) of the
galaxies. Subsequently, a K-means cluster analysis is applied on the nine
independent components to obtain ten distinct and 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, ionisation, metallicity, surface brightness and
structure). We find that our ten groups can be essentially placed into
traditional and empirical classes (from colour-magnitude and emission-line
diagnostic diagrams, early- vs late-types) despite the classical corresponding
features (colour, line ratios and morphology) being not significantly
correlated with the nine Independent Components. More detailed physical
interpretation of the groups will be performed in subsequent papers.
