Learning from Incomplete Data

Authors:
Zoubin Ghahramani;Michael I. Jordan
Affiliations:
-;-
Venue:
Learning from Incomplete Data
Year:
1994

Citing 0
Cited 29

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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, classification, and function approximation