Name
Title | ||
|---|---|---|
Kernelized Supervised Laplacian Eigenmap for Visualization and Classification of Multi-Label Data |
Abstract | ||
|---|---|---|
•We solves the “out-of-sample problem”, inability of handling new data, that previous (supervised) Laplacian eigenmaps have, by a set of linear sums of kernels, and we propose a Kernelized Supervised Laplacian Eigenmap for Multi-Label (KSLE-ML).•We show that nonsingularity of Gram matrix is a sufficient condition for this simulation to be exactly the same as Laplacian eigenmaps.•We reveal that parameter selection is more important than kernel selection in KSLE-ML experimentally, so that RBF kernel solely can be used for the general purpose.•We show a method of separability-guided feature extraction that is based on a high separability of classes in 2D visualization.•We confirm empirically the effectiveness of separability-guided feature extraction by showing that the separability is kept well even for mapping of newly arrived samples without class labels in KSLE-ML. We also demonstrate that the effectiveness increases as the number of samples and the mapping dimension increases. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2021.108399 | Pattern Recognition |
Keywords | DocType | Volume |
Supervised Laplacian eigenmaps,Out-of-sample problem,Multi-label problems,Kernel trick,Separability-guided feature extraction | Journal | 123 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariko Tai | 1 | 0 | 0.34 |
| Mineichi Kudo | 2 | 927 | 116.09 |
| Akira Tanaka | 3 | 38 | 12.20 |
| Hideyuki Imai | 4 | 103 | 25.08 |
| Keigo Kimura | 5 | 0 | 0.34 |