On classification with incomplete data

David Williams, Xuejun Liao, Ya Xue, Lawrence Carin, Balaji Krishnapuram

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

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. © 2007 IEEE.
Original languageEnglish (US)
Pages (from-to)427-436
Number of pages10
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number3
DOIs
StatePublished - Mar 1 2007
Externally publishedYes

Fingerprint Dive into the research topics of 'On classification with incomplete data'. Together they form a unique fingerprint.

Cite this