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Principal component analysis (PCA) is a technique that is widely used for applications such as dimensionality reduction, image compression, feature extraction and data visualization. One of the key issues in the use of PCA for modelling is that it is very sensitive to outliers since its formulation is based on Gaussian density model. Lately, more heavy-tailed distribution (i.e., Student's t-distribution)...
Factor analysis is a statistical covariance modeling technique based on the assumption of normally distributed data. A mixture of factor analyzers can be hence viewed as a special case of Gaussian (normal) mixture models providing a mathematically sound framework for attribute space dimensionality reduction. A significant shortcoming of mixtures of factor analyzers is the vulnerability of normal distributions...
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