Topological Data Analysis

By Moo K. Chung. 2012

479 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/fcd642662d0f5f0edc1c9f127826916c50b31201

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In Computational Theory and Mathematics

Citations in the corpus

A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “3 and Table III shows the results of the TDA.”
A tutorial on kernel density estimation and recent advances (2017, background): “Persistent diagram A persistent diagram [85,99,100] is a diagram summarizing the topological features of a density function p.”
Persistent homology for random fields and complexes (2010, background): “Gunnar Carlsson’s webpage [11], which describes a large Stanford TDA (topological data analyis) project, and a DARPA webpage [16] describing a broad based project, also help explain the reasons why so many people have been so attracted to this direction.”
Statistical Aspects of Wasserstein Distances (2018, methodology): “A Wasserstein metric has also been employed to compare persistence diagrams , a fundamental tool in topological data analysis (see Wasserman (2018) for a recent review) summarising the persistent homology properties of a dataset.”
An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists (2017, background): “, 2009), genomics (Carrière and Rabadán, 2020), chemistry (Lee et al., 2017; Smith et al., 2021), sensor networks De Silva and Ghrist (2007), or transportation (Li et al.”
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.”