Persistence Bag-of-Words for Topological Data Analysis

Bartosz Zielinski, Michal Lipinski, Mateusz Juda, Matthias Zeppelzauer, Pawel Dlotko

IJCAI 2019 pp. 4489-4495

doi:10.24963/IJCAI.2019/624 /ijcai/2019/zielinski2019ijcai-persistence/

Abstract

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.

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Cite

Text

Zielinski et al. "Persistence Bag-of-Words for Topological Data Analysis." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/624

Markdown

[Zielinski et al. "Persistence Bag-of-Words for Topological Data Analysis." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/zielinski2019ijcai-persistence/) doi:10.24963/IJCAI.2019/624

BibTeX

@inproceedings{zielinski2019ijcai-persistence,
  title     = {{Persistence Bag-of-Words for Topological Data Analysis}},
  author    = {Zielinski, Bartosz and Lipinski, Michal and Juda, Mateusz and Zeppelzauer, Matthias and Dlotko, Pawel},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {4489-4495},
  doi       = {10.24963/IJCAI.2019/624},
  url       = {https://mlanthology.org/ijcai/2019/zielinski2019ijcai-persistence/}
}