Accession Number : ADA295618
Title : Learning from Incomplete Data.
Descriptive Note : Memorandum rept.,
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Personal Author(s) : Ghahramani, Zoubin ; Jordan, Michael I.
PDF Url : ADA295618
Report Date : 10 DEC 1994
Pagination or Media Count : 12
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 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.
Descriptors : *STATISTICAL PROCESSES, *LEARNING, MATHEMATICAL MODELS, ALGORITHMS, NEURAL NETS, MAXIMUM LIKELIHOOD ESTIMATION, MIXTURES, ESTIMATES, CLUSTERING, PATTERNS, ARTIFICIAL INTELLIGENCE.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE