Sliced Wasserstein Kernel for Persistence Diagrams
Authors: Mathieu Carrière, Marco Cuturi, Steve Oudot
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare k SW to k PSS and k PWG on several benchmark applications for which PDs have been proven useful. We compare these kernels in terms of classification accuracies and computational cost. |
| Researcher Affiliation | Academia | 1INRIA Saclay 2CREST, ENSAE, Universit e Paris Saclay. |
| Pseudocode | Yes | Algorithm 1 Computation of SWM |
| Open Source Code | No | The paper references LIBSVM: 'Software available at http://www.csie.ntu.edu. tw/ cjlin/libsvm.' but does not provide a direct link to the source code for the methodology described in this paper. |
| Open Datasets | Yes | We use some categories of the mesh segmentation benchmark of Chen et al. (Chen et al., 2009), which contains 3D shapes classified in several categories ( airplane , human , ant ...). |
| Dataset Splits | Yes | The cost factor C is cross-validated in the following grid: {0.001, 0.01, 0.1, 1, 10, 100, 1000}. |
| Hardware Specification | Yes | results are averaged over 10 runs on a 2.4GHz Intel Xeon E5530 Quad Core. |
| Software Dependencies | No | The paper mentions 'LIBSVM (Chang & Lin, 2011)' and 'The GUDHI Project (The GUDHI Project, 2015)' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | The cost factor C is cross-validated in the following grid: {0.001, 0.01, 0.1, 1, 10, 100, 1000}. |