Results 21 to 30 of about 128,421 (254)
Cluster Persistence for Weighted Graphs
Entropy, 2023 Persistent homology is a natural tool for probing the topological characteristics of weighted graphs, essentially focusing on their 0-dimensional homology.
Omer Bobrowski, Primoz Skraba
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Hypothesis testing for topological data analysis [PDF]
Journal of Applied and Computational Topology, 2017 14 pages, 5 figures, 1 ...
Andrew Robinson, Katharine Turner
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Topological data analysis for revealing dynamic brain reconfiguration in MEG data [PDF]
PeerJ, 2023 In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG ...
Ali Nabi Duman, Ahmet E. Tatar
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Topological data analysis of biological aggregation models. [PDF]
PLoS ONE, 2015 We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms.
Chad M Topaz +2 more
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Topological Data Analysis and Clustering
, 2023 This article is intended to be a chapter for a ...
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Topological data analysis and cosheaves [PDF]
Japan Journal of Industrial and Applied Mathematics, 2015 version 2 has 30 pages, 18 figures; 25 pages, 17 figures, submitted to the Japan Journal of Industrial and Applied ...
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Neighborhood hypergraph model for topological data analysis
Computational and Mathematical Biophysics, 2022 Hypergraph, as a generalization of the notions of graph and simplicial complex, has gained a lot of attention in many fields. It is a relatively new mathematical model to describe the high-dimensional structure and geometric shapes of data sets.
Liu Jian, Chen Dong, Li Jingyan, Wu Jie
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Empowering Advanced Driver-Assistance Systems from Topological Data Analysis
Mathematics, 2021 We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments.
Tarek Frahi +7 more
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Topological data analysis [PDF]
Nature Photonics, 2018 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.
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BMC Bioinformatics, 2021 Background This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph.
Ewan Carr +4 more
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