Mixtures of probabilistic principal component analyzers
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Online Model Selection Based on the Variational Bayes
Neural Computation
Prior hyperparameters in Bayesian PCA
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
The disposal of incomplete classification data in teaching evaluation system
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A SVM regression based approach to filling in missing values
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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We apply mixture of principal component analyzers (MPCA) to missing value estimation problems. A variational Bayes (VB) method for MPCA with missing values is developed. The missing values are regarded as hidden variables aud their estimation is done simultaneously with the parameter estimation. It is found that VB method is better than maximum likelihood method by using artificial data. We also applied our method to DNA microarray data and the performance outweighed the conventional k-nearest neighbor method.