Name
Title | ||
|---|---|---|
Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction. |
Abstract | ||
|---|---|---|
•We explore the discriminative feature extraction problem.•A Semi‐Supervised local multi‐manifold Isomap by linear embedding is proposed.•Our model can use labeled and unlabeled data to deliver manifold features.•Our model aims to minimize pairwise intra‐class distances in the same manifold.•Our model aims to maximize the distances between different manifolds. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2017.09.043 | Pattern Recognition |
Keywords | Field | DocType |
Semi-supervised manifold feature extraction,Local multi-manifold Isomap,Linear embedding,Classification | Local tangent space alignment,Dimensionality reduction,Pattern recognition,Feature (computer vision),Feature extraction,Manifold alignment,Artificial intelligence,Nonlinear dimensionality reduction,Feature learning,Mathematics,Machine learning,Isomap | Journal |
Volume | Issue | ISSN |
76 | C | 0031-3203 |
Citations | PageRank | References |
19 | 0.54 | 28 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Zhang | 1 | 133 | 30.68 |
| Zhao Zhang | 2 | 938 | 65.99 |
| Jie Qin | 3 | 167 | 17.38 |
| li zhang | 4 | 49 | 8.10 |
| Bing Li | 5 | 28 | 4.05 |
| fanzhang li | 6 | 75 | 8.73 |