The use of unlabeled data in predictive modeling

Feng Liang, Sayan Mukherjee, Mike West

Research output: Contribution to journalArticlepeer-review

Abstract

The incorporation of unlabeled data in regression and classification analysis is an increasing focus of the applied statistics and machine learning literatures, with a number of recent examples demonstrating the potential for unlabeled data to contribute to improved predictive accuracy. The statistical basis for this semisupervised analysis does not appear to have been well delineated; as a result, the underlying theory and rationale may be underappreciated, especially by nonstatisticians. There is also room for statisticians to become more fully engaged in the vigorous research in this important area of intersection of the statistical and computer sciences. Much of the theoretical work in the literature has focused, for example, on geometric and structural properties of the unlabeled data in the context of particular algorithms, rather than probabilistic and statistical questions. This paper overviews the fundamental statistical foundations for predictive modeling and the general questions associated with unlabeled data, highlighting the relevance of venerable concepts of sampling design and prior specification. This theory, illustrated with a series of central illustrative examples and two substantial real data analyses, shows precisely when, why and how unlabeled data matter.

Original languageEnglish (US)
Pages (from-to)189-205
Number of pages17
JournalStatistical Science
Volume22
Issue number2
DOIs
StatePublished - May 2007

Keywords

  • Bayesian analysis
  • Bayesian kernel regression
  • Latent factor models
  • Mixture models
  • Predictive distribution
  • Semisupervised learning
  • Unlabeled data

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

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