giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

Autor:	Tauzin, Guillaume, Lupo, Umberto, Tunstall, Lewis, Pérez, Julian Burella, Caorsi, Matteo, Reise, Wojciech, Medina-Mardones, Anibal, Dassatti, Alberto, Hess, Kathryn
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Mathematics - Algebraic Topology Statistics - Machine Learning 55-04 (Primary) 55N31 62R40 (Secondary)
Zdroj:	NeurIPS 2020 workshop "Topological Data Analysis and beyond" (https://openreview.net/forum?id=fjQtZJOCTXf); JMLR 22 (https://www.jmlr.org/papers/v22/20-325.html)
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.02551 Zobrazit plný text záznamu View this record from Arxiv