Determination of the Mahalanobis matrix using nonparametric noise estimations

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In this paper, the problem of an optimal transformation of the input space for function approximation problems is addressed. The transformation is defined determining the Mahalanobis matrix that minimizes the variance of noise. To compute variance of the noise, a nonparametric estimator called the Delta Test paradigm is used. The proposed approach is illlustrated on two different benchmarks.

Amaury Lendasse, Francesco Corona, Jin Hao, Nima R

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Delta Test Paradigm | ESANN 2006 | ESANN 2007 | Function Approximation Problems | Optimal Transformation |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2006
Where	ESANN
Authors	Amaury Lendasse, Francesco Corona, Jin Hao, Nima Reyhani, Michel Verleysen

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Determination of the Mahalanobis matrix using nonparametric noise estimations

Delta Test Paradigm | ESANN 2006 | ESANN 2007 | Function Approximation Problems | Optimal Transformation |

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