giotto-tda : a topological data analysis toolkit for machine learning and data exploration

Tauzin, Guillaume (EPFL, Lausanne, Switzerland) ; Lupo, Umberto (L2F SA) ; Tunstall, Lewis (L2F SA) ; Burella Pérez, Julian (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Caorsi, Matteo (L2F SA) ; Medina-Mardones, Anibal M. (EPFL, Lausanne, Switzerland) ; Dassatti, Alberto (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Hess, Kathryn (EPFL, Lausanne, Switzerland)

We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
ReDS - Reconfigurable & embedded Digital Systems
Date:
2021-06
Pagination:
6 p.
Published in:
Journal of Machine Learning Research
Numeration (vol. no.):
2021, vol. 22, pp. 1-6
Appears in Collection:



 Record created 2021-07-07, last modified 2021-07-13

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