Topological Data Analysis

By Larry A. Wasserman. 2016

345 citations recorded by the source corpus.

Topological data analysis (TDA) can broadly be described as a collection of data analysis methods that find structure in data. These methods include clustering, manifold estimation, nonlinear dimension reduction, mode estimation, ridge estimation and persistent homology. This paper reviews some of these methods.

Subfields: Biophysics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/5a82727f9a208d376fdd24b4a2481f4d7b2eee07

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Heather A. Harrington
2. John Healy
3. Leland McInnes
4. Christian Bick
5. Elizabeth Gross
6. Michael T. Schaub

In Computational Theory and Mathematics

1. Heather A. Harrington
2. John Healy
3. Leland McInnes
4. Christian Bick
5. Elizabeth Gross
6. Michael T. Schaub

Citations in the corpus

• What are higher-order networks? (2021, background): “Developing such statistical tools for topology, including hypothesis testing, bootstrapping, and defining shape statistics, is another important, related research area [104,105,236,241,288].”
• Accelerated Hierarchical Density Based Clustering (2017, methodology): “Topological data analysis [8], [59], [63] is a suite of techniques bringing the powerful tool of topology to bear on data analysis problems.”