Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1707851 | Applied Mathematics Letters | 2014 | 6 Pages |
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.
Keywords
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Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Ekaterina Merkurjev, Cristina Garcia-Cardona, Andrea L. Bertozzi, Arjuna Flenner, Allon G. Percus,