Estimating the distribution of Galaxy Morphologies on a continuous space. Issue Volume 10:Issue S306(2014) (1st July 2015) - Vinci, Giuseppe; Freeman, Peter; Newman, Jeffrey; Wasserman, Larry; Genovese, Christopher
Abstract: 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.