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

被引：0

作者：

Tauzin, Guillaume ^{[1
]}

Lupo, Umberto ^{[2
]}

Tunstall, Lewis ^{[2
]}

Perez, Julian Burella ^{[3
]}

Caorsi, Matteo ^{[2
]}

Medina-Mardones, Anibal M. ^{[1
]}

Dassatti, Alberto ^{[3
]}

Hess, Kathryn ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, Lab Topol & Neurosci, Lausanne, Switzerland

[2] L2F SA, Lausanne, Switzerland

[3] HES SO, Sch Management & Engn Vaud, Yverdon, Switzerland

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2021年 / 22卷

关键词：

Topological Data Analysis; Persistent Homology; Mapper; Machine Learning; Data Exploration; !text type='Python']Python[!/text;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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

引用

页数：6

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