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Supervised Probabilistic Principal Component Analysis

2006

Conference Paper

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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.

Author(s): Yu, S. and Yu, K. and Tresp, V. and Kriegel, H-P. and Wu, M.
Book Title: KDD 2006
Journal: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006)
Pages: 464-473
Year: 2006
Month: August
Day: 0
Editors: Ungar, L.
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1150402.1150454
Event Name: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Event Place: Philadelphia, PA, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4069,
  title = {Supervised Probabilistic Principal Component Analysis},
  author = {Yu, S. and Yu, K. and Tresp, V. and Kriegel, H-P. and Wu, M.},
  journal = {Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006)},
  booktitle = {KDD 2006},
  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},
  month_numeric = {8}
}