Abstract
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, classi cation, and function approximation from incomplete data in a principled and e cient manner.These algorithms are based on mixture modeling and make t wo distinct appeals to the Expectation-Maximization (EM) principle (Dempster et al., 1977)|both for the estimation of mixture components and for coping with the missing data.
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Computer science
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Publication Info
- Year
- 1994
- Type
- report
- Citations
- 233
- Access
- Closed
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Cite This
Zoubin Ghahramani,
Michael I. Jordan
(1994).
Learning from Incomplete Data..
.
https://doi.org/10.21236/ada295618
Identifiers
- DOI
- 10.21236/ada295618