Mixture of factor models for joint dimensionality reduction and classification
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In many areas such as machine learning, pattern recognition, information retrieval, and data mining one is interested in extracting a low-dimensional data that is truly representative of the properties of the original high dimensional data. For example, one application could be extracting representative low-dimensional features of underwater objects from sonar imagery suitable for detection and classiﬁcation. This is a diﬃcult problem due to various factors such as variations in the operating and environmental conditions, presence of spatially varying clutter, and variations in object shapes, ...