Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
2006 Special issue: Exploratory analysis of climate data using source separation methods
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Variational Bayesian mixture of robust CCA models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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We present a probabilistic model for robust principal component analysis (PCA) in which the observation noise is modelled by Student-t distributions that are independent for different data dimensions. A heavy-tailed noise distribution is used to reduce the negative effect of outliers. Intractability of posterior evaluation is solved using variational Bayesian approximation methods. We show experimentally that the proposed model can be a useful tool for PCA preprocessing for incomplete noisy data. We also demonstrate that the assumed noise model can yield more accurate reconstructions of missing values: Corrupted dimensions of a "bad" sample may be reconstructed well from other dimensions of the same data vector. The model was motivated by a real-world weather dataset which was used for comparison of the proposed technique to relevant probabilistic PCA models.