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

Tauzin, Guillaume (INAIT SA ; EPFL, Lausanne, Switzerland) ; Lupo, Umberto (EPFL, Lausanne, Switzerland ; 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) ; Reise, Wojciech (DataShape, Inira Saclay, Île-de-France, France) ; 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 itsstrong 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:
Conference Type:
published full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
ReDS - Reconfigurable & embedded Digital Systems
Publisher:
Vancouver, Canada, 6-12 December 2020
Date:
2020-12
Vancouver, Canada
6-12 December 2020
Pagination:
7 p.
Published in:
Proceedings of 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 6-12 December 2020, Vancouver, Canada
Appears in Collection:



 Record created 2021-02-09, last modified 2021-02-12

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