Estimating the distribution of Galaxy Morphologies on a continuous space
  The incredible variety of galaxy shapes cannot be summarized by human defined
discrete classes of shapes without causing a possibly large loss of
information. Dictionary learning and sparse coding allow us to reduce the high
dimensional space of shapes into a manageable low dimensional continuous vector
space. Statistical inference can be done in the reduced space via probability
distribution estimation and manifold estimation.
