Name
Abstract | ||
|---|---|---|
•Our algorithm reveals underlying smooth manifolds of high-dimensional data.•The smoothing spline approach ensures the smoothness of a corrupted manifold.•The algorithm handles noise and sparsity gracefully.•Performance versus neighborhood size, smoothness, sparsity, and noise are analyzed.•Compared to Isomap, embeddings of face images and hand written digits are improved. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2018.10.020 | Pattern Recognition |
Keywords | DocType | Volume |
Manifold,Nonlinear dimensionality reduction,Smoothing spline,Geodesics,Noisy measurements | Journal | 87 |
Issue | ISSN | Citations |
1 | 0031-3203 | 1 |
PageRank | References | Authors |
0.35 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gajamannage Kelum | 1 | 2 | 1.73 |
| Randy Paffenroth | 2 | 99 | 14.17 |
| Erik M. Bollt | 3 | 213 | 29.07 |