[ACM Press the 12th ACM SIGKDD international conference - Philadelphia, PA, USA (2006.08.20-2006.08.23)] Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06 - Supervised probabilistic principal component analysis

✍ Scribed by Yu, Shipeng; Yu, Kai; Tresp, Volker; Kriegel, Hans-Peter; Wu, Mingrui

• Book ID: 118148762
• Publisher: ACM Press
• Year: 2006
• Weight: 891 KB
• Volume: 0
• Category: Article
• ISBN-13: 9781595933393
• DOI: 10.1145/1150402.1150454

✦ Synopsis

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.