Title: Incremental online PCA for automatic motion learning of eigen behaviour

Authors: Xianhua Jiang, Yuichi Motai

Addresses: Department of Electrical and Computer Engineering, University of Vermont, Burlington, VT 05405, USA. ' Department of Electrical and Computer Engineering, University of Vermont, Burlington, VT 05405, USA

Abstract: This paper presents an online learning framework for the behaviour of an articulated body by capturing its motion using real-time video. In our proposed framework, supervised learning is first utilised during an offline learning phase for small instances using principal component analysis (PCA); then we apply a new incremental PCA technique during an online learning phase. Rather than storing all the previous instances, our online method just keeps the eigenspace and reconstructs the space using only the new instance. We can add numerical new training instances while maintaining the reasonable dimensions. The experimental results demonstrate the feasibility and merits.

Keywords: incremental learning; motion analysis; principal component analysis; PCA; behaviour editor; learning by imitation; automatic learning; real time; eigen behaviour.

DOI: 10.1504/IJISTA.2007.012490

International Journal of Intelligent Systems Technologies and Applications, 2007 Vol.2 No.2/3, pp.296 - 312

Available online: 19 Feb 2007 *

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