Abstract
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 and 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 UNA microarray data and the performance outweighed the conventional A;-nearest neighbor method. © Springer-Verlag Berlin Heidelberg 2002.
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CITATION STYLE
Oba, S., Sato, M. A., Takemasa, I., Monden, M., Matsubara, K. I., & Ishii, S. (2002). Missing value estimation using mixture of PCAs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 492–497). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_80
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