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.
Einzelheiten
Titel
giotto-tda : a topological data analysis toolkit for machine learning and data exploration
Autor(en)/ in(nen)
Tauzin, Guillaume (INAIT SA ; EPFL, Lausanne, Switzerland) Lupo, Umberto (EPFL, Lausanne, Switzerland ; L2F SA) Tunstall, Lewis (L2F Sa) Burella Pérez, Julian (School of Engineering and Management Vaud, HES-SO, University of Applied Sciences and Arts 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 Engineering and Management Vaud, HES-SO, University of Applied Sciences and Arts Western Switzerland) Hess, Kathryn (EPFL, Lausanne, Switzerland)
Datum
2020-12
Veröffentlich in
Proceedings of 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 6-12 December 2020, Vancouver, Canada
Verlag
Vancouver, Canada, 6-12 December 2020
Umfang
7 p.
Vorgestellt auf
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020-12-06, 2020-12-12