Kernel Matrix Completion by Semidefinite Programming
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Semi-supervised learning with graphs
Semi-supervised learning with graphs
A selective Bayes Classifier for classifying incomplete data based on gain ratio
Knowledge-Based Systems
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
A selective classifier for incomplete data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A hybrid selective classifier for categorizing incomplete data
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Learn++.MF: A random subspace approach for the missing feature problem
Pattern Recognition
Predicting incomplete gene microarray data with the use of supervised learning algorithms
Pattern Recognition Letters
Classification with Incomplete Data Using Dirichlet Process Priors
The Journal of Machine Learning Research
A robust missing value imputation method for noisy data
Applied Intelligence
Semiconducting bilinear deep learning for incomplete image recognition
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Estimating conversion rate in display advertising from past erformance data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
WIMP: Web server tool for missing data imputation
Computer Methods and Programs in Biomedicine
Advances in Artificial Intelligence
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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.