On classification with incomplete data.

Published

Journal Article

We address the incomplete-data problem in which feature vectors to be classified are missing data (features). A (supervised) logistic regression algorithm for the classification of incomplete data is developed. Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the observed data). Conditional density functions are estimated using a Gaussian mixture model (GMM), with parameter estimation performed using both Expectation-Maximization (EM) and Variational Bayesian EM (VB-EM). The proposed supervised algorithm is then extended to the semisupervised case by incorporating graph-based regularization. The semisupervised algorithm utilizes all available data-both incomplete and complete, as well as labeled and unlabeled. Experimental results of the proposed classification algorithms are shown.

Full Text

Duke Authors

Cited Authors

  • Williams, D; Liao, X; Xue, Y; Carin, L; Krishnapuram, B

Published Date

  • March 2007

Published In

Volume / Issue

  • 29 / 3

Start / End Page

  • 427 - 436

PubMed ID

  • 17224613

Pubmed Central ID

  • 17224613

Electronic International Standard Serial Number (EISSN)

  • 1939-3539

International Standard Serial Number (ISSN)

  • 0162-8828

Digital Object Identifier (DOI)

  • 10.1109/tpami.2007.52

Language

  • eng