Supervised probabilistic principal component analysis

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 S2 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 S2 PPCA are compared with other supervised projection methods on various learning tasks, and they show n...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel, Mingrui Wu
• Keywords: Data Mining, KDD 2006, Probabilistic Pca Model, Semi-supervised Pca Model, Supervised Pca Model