Name
Title | ||
|---|---|---|
Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm |
Abstract | ||
|---|---|---|
We study the convergence and the rate of convergence of a particular manifold- based learning algorithm: LTSA (12). The main technical tool is the perturbation analysis on the linear invariant subspace that corresponds to the solution of LTSA. We derive the upper bound for errors under the worst case for LTSA; it naturally leads to a convergence result. We then derive the rate of convergence for LTSA in a special case. |
Year | Venue | Keywords |
|---|---|---|
2008 | NIPS | rate of convergence,dimension reduction,upper bound,perturbation analysis |
Field | DocType | Citations |
Convergence (routing),Mathematical optimization,Normal convergence,Mathematical analysis,Compact convergence,Algorithm,Convergence tests,Invariant subspace,Rate of convergence,Nonlinear dimensionality reduction,Mathematics,Modes of convergence | Conference | 3 |
PageRank | References | Authors |
0.41 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Smith | 1 | 96 | 13.91 |
| Xiaoming Huo | 2 | 157 | 24.83 |
| Hongyuan Zha | 3 | 6703 | 422.09 |