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NIPS
2008

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Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm

13 years 5 months ago

Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm

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We study the convergence and the rate of convergence of a local manifold learning algorithm: LTSA [13]. The main technical tool is the perturbation analysis on the linear invariant subspace that corresponds to the solution of LTSA. We derive a worst-case upper bound of errors for LTSA which naturally leads to a convergence result. We then derive the rate of convergence for LTSA in a special case.

Andrew Smith, Xiaoming Huo, Hongyuan Zha

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Convergence | Information Technology | Linear Invariant Subspace | Manifold Learning Algorithm | NIPS 2008 |

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Added	30 Oct 2010
Updated	30 Oct 2010
Type	Conference
Year	2008
Where	NIPS
Authors	Andrew Smith, Xiaoming Huo, Hongyuan Zha

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