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Conference Paper

Supervised Probabilistic Principal Component Analysis

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84321

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

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Citation

Yu, S., Yu K, Tresp V, Kriegel, H.-P., & Wu, M. (2006). Supervised Probabilistic Principal Component Analysis. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), 464-473.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D099-1
Abstract
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 S^2PPCA, 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 S^2PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.