1998 LocalAdaptiveSubspaceRegression

Subject Headings: Nonparametric Learning Algorithm, Nonparametric Statistical Regression Algorithm.

Notes

Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as biological movement systems. So far, due to sparsity of data in high dimensional spaces, learning in such settings required a significant amount of prior knowledge about the learning task, usually provided by a human expert. In this paper we suggest a partial revision of the view. Based on empirical studies, we observed that, despite being globally high dimensional and sparse, data distributions from physical movement systems are locally low dimensional and dense. Under this assumption, we derive a learning algorithm, Locally Adaptive Subspace Regression, that exploits this property by combining a dynamically growing local dimensionality reduction technique as a preprocessing step with a nonparametric learning technique, locally weighted regression, that also learns the region of validity of the regression. The usefulness of the algorithm and the validity of its assumptions are illustrated for a synthetic data set, and for data of the inverse dynamics of human arm movements and an actual 7 degree-of-freedom anthropomorphic robot arm.
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… Based on this, we developed a nonparametric learning algorithm which is targeted to make use of such locally low dimensional distributions. …

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 LocalAdaptiveSubspaceRegression	Sethu Vijayakumar Stefan Schaal			Local Adaptive Subspace Regression			http://www-clmc.usc.edu/publications/V/vijayakumar-NPL1998.pdf	10.1023/A:1009696221209