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.
The authors in REF proposed a supervised PCA model called SPPCA, whereby they extended PCA to incorporate label/class information into the projection phase.
Supervised probabilistic principal component analysis
KDD '06
Computer Science