Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7202 |
| Date | 24 January 1995 |
| Creators | Ghahramani, Zoubin, Jordan, Michael I. |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 11 p., 388268 bytes, 515095 bytes, application/postscript, application/pdf |
| Relation | AIM-1509, CBCL-108 |
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