Name
Abstract | ||
|---|---|---|
•Our algorithm detects a low-dimensional manifold that lies within a set of bounds.•The bounds are derived using a given high-dimensional dataset of a point cloud.•A matrix representing the distances on a low-dimensional manifold is low-rank.•Our method recovers a partially observed distance matrix using fully observed entries.•Low-rank matrix completion is used to recover partially observed distance matrices. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107661 | Pattern Recognition |
Keywords | DocType | Volume |
Manifold,Low-rank matrix completion,Positive semi-definite,Truncated nuclear norm,Gramian | Journal | 111 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gajamannage Kelum | 1 | 2 | 1.73 |
| Randy Paffenroth | 2 | 99 | 14.17 |