Search Results for author: Nina Otter

Found 6 papers, 4 papers with code

On the effectiveness of persistent homology

1 code implementation21 Jun 2022 Renata Turkeš, Guido Montúfar, Nina Otter

The goal of this work is to identify some types of problems where PH performs well or even better than other methods in data analysis.

Topological Data Analysis

Can neural networks learn persistent homology features?

no code implementations NeurIPS Workshop TDA_and_Beyond 2020 Guido Montúfar, Nina Otter, Yuguang Wang

Topological data analysis uses tools from topology -- the mathematical area that studies shapes -- to create representations of data.

Topological Data Analysis

Stratifying multiparameter persistent homology

no code implementations24 Aug 2017 Heather A. Harrington, Nina Otter, Hal Schenck, Ulrike Tillmann

A fundamental tool in topological data analysis is persistent homology, which allows extraction of information from complex datasets in a robust way.

Algebraic Topology Commutative Algebra 55B55, 68U05, 68Q17, 13P25 (primary)

A roadmap for the computation of persistent homology

1 code implementation30 Jun 2015 Nina Otter, Mason A. Porter, Ulrike Tillmann, Peter Grindrod, Heather A. Harrington

We give a friendly introduction to PH, navigate the pipeline for the computation of PH with an eye towards applications, and use a range of synthetic and real-world data sets to evaluate currently available open-source implementations for the computation of PH.

Algebraic Topology Computational Geometry Data Analysis, Statistics and Probability Quantitative Methods

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