Name
Abstract | ||
|---|---|---|
We present two graph-based algorithms for multiclass segmentation of high-dimensional data, motivated by the binary diffuse interface model. One algorithm generalizes Ginzburg–Landau (GL) functional minimization on graphs to the Gibbs simplex. The other algorithm uses a reduction of GL minimization, based on the Merriman–Bence–Osher scheme for motion by mean curvature. These yield accurate and efficient algorithms for semi-supervised learning. Our algorithms outperform existing methods, including supervised learning approaches, on the benchmark datasets that we used. We refer to Garcia-Cardona (2014) for a more detailed illustration of the methods, as well as different experimental examples. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.aml.2014.02.008 | Applied Mathematics Letters |
Keywords | Field | DocType |
Segmentation,Graphs,Ginzburg–Landau functional,MBO scheme,Convex splitting | Mathematical optimization,Clustering high-dimensional data,Two-graph,Mathematical analysis,Segmentation,Mean curvature,Algorithm,Simplex,Supervised learning,Minification,Mathematics,Binary number | Journal |
Volume | ISSN | Citations |
33 | 0893-9659 | 6 |
PageRank | References | Authors |
0.43 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ekaterina Merkurjev | 1 | 43 | 3.07 |
| Cristina Garcia-Cardona | 2 | 48 | 5.00 |
| Andrea L. Bertozzi | 3 | 486 | 61.55 |
| Arjuna Flenner | 4 | 37 | 3.78 |
| Allon G. Percus | 5 | 288 | 24.31 |