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.

Report Date:

1994-12-10

Pagination or Media Count:

12.0

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
• Psychology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE