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Abstract: this paper we suggest a partial revision of the view. Based on empirical studies, 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 local dimensionality reduction as a preprocessing step with a nonparametric learning technique, locally weighted regression. The usefulness of the algorithm and the validity of its assumptions are illustrated for a... (Update)
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BibTeX entry: (Update)
Vijayakumar, S., & Schaal, S. (submitted). "Local dimensionality reduction for locally weighted learning." Wahba, G., & Wold, S. (1975). "A completely automatic french curve: Fitting spline functions by cross-validation." Communications in Statistics, 4 (1), . http://citeseer.ist.psu.edu/article/vijayakumar97local.html More
@misc{ vijayakumar75local,
author = "S. Vijayakumar and S. Schaal",
title = "Local dimensionality reduction for locally weighted learning",
text = "Vijayakumar, S., & Schaal, S. (submitted). Local dimensionality reduction
for locally weighted learning. Wahba, G., & Wold, S. (1975). A completely
automatic french curve: Fitting spline functions by cross-validation. Communications
in Statistics, 4 (1), .",
year = "1975",
url = "citeseer.ist.psu.edu/article/vijayakumar97local.html" }
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