nips nips2002 nips2002-101 nips2002-101-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski
Abstract: Missing data is common in real-world datasets and is a problem for many estimation techniques. We have developed a variational Bayesian method to perform Independent Component Analysis (ICA) on high-dimensional data containing missing entries. Missing data are handled naturally in the Bayesian framework by integrating the generative density model. Modeling the distributions of the independent sources with mixture of Gaussians allows sources to be estimated with different kurtosis and skewness. The variational Bayesian method automatically determines the dimensionality of the data and yields an accurate density model for the observed data without overfitting problems. This allows direct probability estimation of missing values in the high dimensional space and avoids dimension reduction preprocessing which is not feasible with missing data.
[1] Kwokleung Chan, Te-Won Lee, and Terrence J. Sejnowski. Variational learning of clusters of undercomplete nonsymmetric independent components. Journal of Machine Learning Research, 3:99–114, 2002.
[2] Rizwan A. Choudrey and Stephen J. Roberts. Flexible Bayesian independent component analysis for blind source separation. In 3rd International Conference on Independent Component Analysis and Blind Signal Separation, pages 90–95, San Diego, Dec. 09-12 2001.
[3] Z. Ghahramani and M. Jordan. Learning from incomplete data. Technical Report CBCL Paper No. 108, Center for Biological and Computational Learning, Massachusetts Institute of Technology, 1994.
[4] Aapo Hyvarinen, Juha Karhunen, and Erkki Oja. Independent Component Analysis. J. Wiley, New York, 2001.
[5] R. J. A. Little and D. B. Rubin. Statistical Analysis with Missing Data. Wiley, New York, 1987.
[6] Max Welling and Markus Weber. Independent component analysis of incomplete data. In 1999 6th Joint Symposium on Neural Compuatation Proceedings, volume 9, pages 162–168. UCSD, May. 22 1999.