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Conference Paper

Supervised Probabilistic Principal Component Analysis


Wu,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Yu, S., Yu, K., Tresp, V., Kriegel, H.-P., & Wu, M. (2006). Supervised Probabilistic Principal Component Analysis. In T. Eliassi-Rad, L. Ungar, M. Craven, & D. Gunopulos (Eds.), KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 464-473). New York, NY, USA: ACM Press.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D099-1
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is however not able to use this information. The problem is more interesting if only part of the input data are labeled, i.e., in a semi-supervised setting. In this paper we propose a supervised PCA model called SPPCA and a semi-supervised PCA model called S2PPCA, both of which are extensions of a probabilistic PCA model. The proposed models are able to incorporate the label information into the projection phase, and can naturally handle multiple outputs (i.e., in multi-task learning problems). We derive an efficient EM learning algorithm for both models, and also provide theoretical justifications of the model behaviors. SPPCA and S2PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.