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. 
Giuseppe Vinci, Peter Freeman, Jeffrey Newman, Larry Wasserman and   Christopher Genovese
astro-ph.GA astro-ph.CO stat.AP stat.CO stat.ML