A stable multi-scale kernel for topological machine learning

@article{Reininghaus2015ASM,
  title={A stable multi-scale kernel for topological machine learning},
  author={Jan Reininghaus and S. Huber and U. Bauer and R. Kwitt},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={4741-4748}
}
  • Jan Reininghaus, S. Huber, +1 author R. Kwitt
  • Published 2015
  • Computer Science, Mathematics
  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Topological data analysis offers a rich source of valuable information to study vision problems. [...] Key Method We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes.Expand
Statistical Topological Data Analysis - A Kernel Perspective
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Deep Learning with Topological Signatures
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