regression on Manifold using Kernel Dimension Reduction: Difference between revisions
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An Algorithm for finding a new linear map for dimension reduction. | |||
===Introduction=== | |||
This paper <ref>[http://www.machinelearning.org/proceedings/icml2007/papers/491.pdf] Jen Nilsson, Fei Sha, Michael I. Jordan, Regression on Manifold using Kernel Dimension Reduction, 2007 - cs.utah.edu | |||
</ref>introduces a new algorithm for for discovering a manifold that best preserves the information relevant to a non-linear regression. The approach introduced by the authors involves combining the machinery of Kernel Dimesnion Reduction (KDR) with Laplacian Eigenmaps by optimizing the cross-covariance operators in kernel feature space. | |||
===Sufficient Dimension Reduction=== | |||
(this section will be updated shortly) | |||
===Kernel Dimension Reduction=== | |||
(this section will be updated shortly) | |||
===Manifold Learning=== | |||
(this section will be updated shortly) | |||
===Manifold Kernel Dimension Reduction=== | |||
(this section will be updated shortly) | |||
===Examples=== | |||
(this section will be updated shortly) | |||
===SUmmary=== | |||
(this section will be updated shortly) | |||
===Further Research=== | |||
Revision as of 17:02, 18 July 2009
An Algorithm for finding a new linear map for dimension reduction.
Introduction
This paper <ref>[1] Jen Nilsson, Fei Sha, Michael I. Jordan, Regression on Manifold using Kernel Dimension Reduction, 2007 - cs.utah.edu </ref>introduces a new algorithm for for discovering a manifold that best preserves the information relevant to a non-linear regression. The approach introduced by the authors involves combining the machinery of Kernel Dimesnion Reduction (KDR) with Laplacian Eigenmaps by optimizing the cross-covariance operators in kernel feature space.
Sufficient Dimension Reduction
(this section will be updated shortly)
Kernel Dimension Reduction
(this section will be updated shortly)
Manifold Learning
(this section will be updated shortly)
Manifold Kernel Dimension Reduction
(this section will be updated shortly)
Examples
(this section will be updated shortly)
SUmmary
(this section will be updated shortly)