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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Andrew Smith, Xiaoming Huo, Hongyuan Zha
• Keywords: Convergence, Information Technology, Linear Invariant Subspace, Manifold Learning Algorithm, NIPS 2008