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

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

Authors

Andrew Smith, Hongyuan Zha, Xiao-ming Wu

Abstract

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.