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Neural Processing Letters

dc.contributor.authorHoffmann, Heiko
dc.contributor.authorSchaal, Stefan
dc.contributor.authorVijayakumar, Sethu
dc.date.accessioned2010-08-31T14:07:27Z
dc.date.available2010-08-31T14:07:27Z
dc.date.issued2009
dc.identifier.urihttp://www.springerlink.com/content/lp158m5149m38460/en
dc.identifier.urihttp://hdl.handle.net/1842/3704
dc.description.abstractLocally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many local models need to be maintained simultaneously. Thus, local linear dimensionality reduction combined with locally-weighted regression seems to be a promising solution. In this context, we review linear dimensionalityreduction methods, compare their performance on non-parametric locally-linear regression, and discuss their ability to extend to incremental learning. The considered methods belong to the following three groups: (1) reducing dimensionality only on the input data, (2) modeling the joint input-output data distribution, and (3) optimizing the correlation between projection directions and output data. Group 1 contains principal component regression (PCR); group 2 contains principal component analysis (PCA) in joint input and output space, factor analysis, and probabilistic PCA; and group 3 contains reduced rank regression (RRR) and partial least squares (PLS) regression. Among the tested methods, only group 3 managed to achieve robust performance even for a non-optimal number of components (factors or projection directions). In contrast, group 1 and 2 failed for fewer components since these methods rely on the correct estimate of the true intrinsic dimensionality. In group 3, PLS is the only method for which a computationally-efficient incremental implementation exists.en
dc.language.isoenen
dc.publisherSpringeren
dc.subjectInformaticsen
dc.subjectComputer Scienceen
dc.titleLocal Dimensionality Reduction for Non-Parametric Regressionen
dc.typeArticleen
dc.identifier.doi10.1007/s11063-009-9098-0en
rps.issue2en
rps.volume29en
rps.titleNeural Processing Lettersen
dc.extent.pageNumbers109-131en
dc.date.updated2010-08-31T14:07:27Z
dc.identifier.eIssn13704621en


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