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2008

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

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