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Incremental Sparsification for Real-time Online Model Learning

Duy Nguyen-Tuong; Jan Peters
In: Yee Whye Teh; D. Mike Titterington (Hrsg.). Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics (AISTATS-2010), May 13-15, Italy, Pages 557-564, JMLR Proceedings, Vol. 9,, 2010.


Online model learning in real-time is required by many applications, for example, robot tracking control. It poses a difficult problem, as fast and incremental online regression with large data sets is the essential component and cannot be realized by straightforward usage of off-the-shelf machine learning methods such as Gaussian process regression or support vector regression. In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for large scale real-time model learning. The proposed approach combines a sparsification method based on an independency measure with a large scale database. In combination with an incremental learning approach such as sequential support vector regression, we obtain a regression method which is applicable in real-time online learning. It exhibits competitive learning accuracy when compared with standard regression techniques. Implementation on a real robot emphasizes the applicability of the proposed approach in real-time online model learning for real world systems.

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