Learning to Classify Galaxy Shapes Using the EM Algorithm
Abstract
We describe the application of probabilistic model-based learning to the problem of automatically identifying classes of galaxies, based on both morphological and pixel intensity characteristics. The EM algorithm can be used to learn how to spatially orient a set of galaxies so that they are geometrically aligned. We augment this “ordering-model” with a mixture model on objects, and demonstrate how classes of galaxies can be learned in an unsupervised manner using a two-level EM algorithm. The resulting models provide highly accurate classi£cation of galaxies in cross-validation experiments.
Cite
Text
Kirshner et al. "Learning to Classify Galaxy Shapes Using the EM Algorithm." Neural Information Processing Systems, 2002.Markdown
[Kirshner et al. "Learning to Classify Galaxy Shapes Using the EM Algorithm." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/kirshner2002neurips-learning/)BibTeX
@inproceedings{kirshner2002neurips-learning,
title = {{Learning to Classify Galaxy Shapes Using the EM Algorithm}},
author = {Kirshner, Sergey and Cadez, Igor V. and Smyth, Padhraic and Kamath, Chandrika},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1521-1528},
url = {https://mlanthology.org/neurips/2002/kirshner2002neurips-learning/}
}