  We examine a general framework for visualizing datasets of high (> 2)
dimensionality, and demonstrate it using the morphology of galaxies at moderate
redshifts. The distributions of various populations of such galaxies are
examined in a space spanned by four purely morphological parameters. Galaxy
images are taken from the Hubble Space Telescope (HST) Wide Field Planetary
Camera 2 (WFPC2) in the I band (F814W). Since we have little prior knowledge on
how galaxies are distributed in morphology space we use an unsupervised
learning method (a variant of Kohonen's Self Organizing Maps, or SOMs). This
method allows the data to organize themselves onto a two-dimensional space
while conserving most of the topology of the original space. It thus enables us
to visualize the distribution of galaxies and study it more easily. The process
is fully automated, does not rely on any kind of eyeball classification and is
readily applicable to large numbers of images. We apply it to a sample of 2934
galaxies, and find that morphology correlates well with the apparent magnitude
distribution and to lesser extents with color and bulge dominance. The
resulting map traces a morphological sequence similar to the Hubble Sequence,
albeit two dimensional. We use the SOM as a diagnostic tool, and rediscover a
population of bulge-dominated galaxies with morphologies characteristic of
peculiar galaxies. This is achieved without recourse to eyeball classification.
We also examine the effect of noise on the resulting SOM, and conclude that
down to I magnitude of 24 our results are reliable. We propose using this
method as a framework into which more physical data can be incorporated when
they become available. Hopefully, this will lead to a deeper understanding of
galaxy evolution.
