Multi-Parameter Persistent Homology Is Practical (Extended Abstract)

Abstract

Multi-parameter persistent homology is a branch of topological data analysis that is notorious for being more difficult than the standard (one-parameter) version, both in theory and for algorithmic problems. We report on three ongoing projects that demonstrates that multi-parameter method are applicable to large data sets. For instance, natural bi-filtrations generalizing Vietoris-Rips or alpha filtrations for hundred of thousands of points can be decomposed within seconds in their indecomposable parts.

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Cite

Text

Kerber. "Multi-Parameter Persistent Homology Is Practical (Extended Abstract)." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.

Markdown

[Kerber. "Multi-Parameter Persistent Homology Is Practical (Extended Abstract)." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.](https://mlanthology.org/neuripsw/2020/kerber2020neuripsw-multiparameter/)

BibTeX

@inproceedings{kerber2020neuripsw-multiparameter,
  title     = {{Multi-Parameter Persistent Homology Is Practical (Extended Abstract)}},
  author    = {Kerber, Michael},
  booktitle = {NeurIPS 2020 Workshops: TDA_and_Beyond},
  year      = {2020},
  url       = {https://mlanthology.org/neuripsw/2020/kerber2020neuripsw-multiparameter/}
}