Supervised Learning from Incomplete Data via an EM Approach
Abstract
Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data set.s. VVe use mixture models for the den(cid:173) sity estimates and make two distinct appeals to the Expectation(cid:173) Maximization (EM) principle (Dempster et al., 1977) in deriving a learning algorithm-EM is used both for the estimation of mix(cid:173) ture components and for coping wit.h missing dat.a. The result(cid:173) ing algorithm is applicable t.o a wide range of supervised as well as unsupervised learning problems. Result.s from a classification benchmark-t.he iris data set-are presented.
Cite
Text
Ghahramani and Jordan. "Supervised Learning from Incomplete Data via an EM Approach." Neural Information Processing Systems, 1993.Markdown
[Ghahramani and Jordan. "Supervised Learning from Incomplete Data via an EM Approach." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/ghahramani1993neurips-supervised/)BibTeX
@inproceedings{ghahramani1993neurips-supervised,
title = {{Supervised Learning from Incomplete Data via an EM Approach}},
author = {Ghahramani, Zoubin and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {120-127},
url = {https://mlanthology.org/neurips/1993/ghahramani1993neurips-supervised/}
}