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 http://github.com/giotto-ai/giotto-tda. © 2021 Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Perez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, and Kathryn Hess.

giotto-tda: A topological data analysis toolkit for machine learning and data exploration / Tauzin, G.; Lupo, U.; Tunstall, L.; Perez, J. B.; Caorsi, M.; Medina-Mardones, A. M.; Dassatti, A.; Hess, K.. - In: JOURNAL OF MACHINE LEARNING RESEARCH. - ISSN 1532-4435. - 22:(2021), pp. 1-6.

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

Caorsi M.;
2021-01-01

Abstract

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 http://github.com/giotto-ai/giotto-tda. © 2021 Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Perez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, and Kathryn Hess.
2021
22
1
6
https://arxiv.org/abs/2004.02551
Tauzin, G.; Lupo, U.; Tunstall, L.; Perez, J. B.; Caorsi, M.; Medina-Mardones, A. M.; Dassatti, A.; Hess, K.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/142053
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