Estimating the distribution of Galaxy Morphologies on a continuous space
Astrophysics of Galaxies
2014-07-01 v1 Cosmology and Nongalactic Astrophysics
Applications
Computation
Machine Learning
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.
Keywords
Cite
@article{arxiv.1406.7536,
title = {Estimating the distribution of Galaxy Morphologies on a continuous space},
author = {Giuseppe Vinci and Peter Freeman and Jeffrey Newman and Larry Wasserman and Christopher Genovese},
journal= {arXiv preprint arXiv:1406.7536},
year = {2014}
}
Comments
4 pages, 3 figures, Statistical Challenges in 21st Century Cosmology, Proceedings IAU Symposium No. 306, 2014