@inproceedings{4069,
  title = {Supervised Probabilistic Principal Component Analysis},
  journal = {Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006)},
  booktitle = {KDD 2006},
  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$^2$PPCA, 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$^2$PPCA are
  compared with other supervised projection methods on various
  learning tasks, and show not only promising performance but also
  good scalability.},
  pages = {464-473},
  editors = {Ungar, L. },
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = aug,
  year = {2006},
  author = {Yu, S. and Yu, K. and Tresp, V. and Kriegel, H-P. and Wu, M.},
  doi = {10.1145/1150402.1150454},
  month_numeric = {8}
}