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On classification with incomplete data.

Publication ,  Journal Article
Williams, D; Liao, X; Xue, Y; Carin, L; Krishnapuram, B
Published in: IEEE transactions on pattern analysis and machine intelligence
March 2007

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.

Duke Scholars

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

March 2007

Volume

29

Issue

3

Start / End Page

427 / 436

Related Subject Headings

  • Sensitivity and Specificity
  • Sample Size
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Logistic Models
  • Information Storage and Retrieval
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Computer Simulation
  • Artificial Intelligence & Image Processing
 

Citation

APA
Chicago
ICMJE
MLA
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Williams, D., Liao, X., Xue, Y., Carin, L., & Krishnapuram, B. (2007). On classification with incomplete data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 427–436. https://doi.org/10.1109/tpami.2007.52
Williams, David, Xuejun Liao, Ya Xue, Lawrence Carin, and Balaji Krishnapuram. “On classification with incomplete data.IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 3 (March 2007): 427–36. https://doi.org/10.1109/tpami.2007.52.
Williams D, Liao X, Xue Y, Carin L, Krishnapuram B. On classification with incomplete data. IEEE transactions on pattern analysis and machine intelligence. 2007 Mar;29(3):427–36.
Williams, David, et al. “On classification with incomplete data.IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 3, Mar. 2007, pp. 427–36. Epmc, doi:10.1109/tpami.2007.52.
Williams D, Liao X, Xue Y, Carin L, Krishnapuram B. On classification with incomplete data. IEEE transactions on pattern analysis and machine intelligence. 2007 Mar;29(3):427–436.

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

March 2007

Volume

29

Issue

3

Start / End Page

427 / 436

Related Subject Headings

  • Sensitivity and Specificity
  • Sample Size
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Logistic Models
  • Information Storage and Retrieval
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Computer Simulation
  • Artificial Intelligence & Image Processing