EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Mixtures of probabilistic principal component analyzers
Neural Computation
Robust mixture modelling using the t distribution
Statistics and Computing
Robust Parameterized Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
Proceedings of the 24th international conference on Machine learning
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
Robust Visual Mining of Data with Error Information
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Bayesian Robust PCA for Incomplete Data
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Robust probabilistic PCA with missing data and contribution analysis for outlier detection
Computational Statistics & Data Analysis
A new probabilistic approach to on-line learning in artificial neural networks
ASMCSS'09 Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, Circuits, Systems and Signals
Improving the robustness to outliers of mixtures of probabilistic PCAs
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Robust mixture clustering using Pearson type VII distribution
Pattern Recognition Letters
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
Robust Bayesian Clustering for Replicated Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bayesian Robust PCA of Incomplete Data
Neural Processing Letters
Bayesian Canonical correlation analysis
The Journal of Machine Learning Research
Ensemble canonical correlation analysis
Applied Intelligence
A comparative study of novel robust clustering algorithms
Intelligent Data Analysis
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Principal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, probabilistic reformulations of these methods have been proposed (Roweis, 1998; Tipping & Bishop, 1999b; Bach & Jordan, 2005). They are based on a Gaussian density model and are therefore, like their non-probabilistic counterpart, very sensitive to atypical observations. In this paper, we introduce robust probabilistic principal component analysis and robust probabilistic canonical correlation analysis. Both are based on a Student-t density model. The resulting probabilistic reformulations are more suitable in practice as they handle outliers in a natural way. We compute maximum likelihood estimates of the parameters by means of the EM algorithm.