Top Tweets for #MergeTrees
๐คWasserstein auto-encoders of #MergeTrees can reliably parameterize collections of merge trees, with applications in #DimensionalityReduction or compression.
More details in our latest @ieee_tvcg paper๐
https://t.co/b69h78HFQg
#TopologicalDataAnalysis #datascience
๐ข Announcement: our paper on Wasserstein Auto-Encoders of #MergeTrees and #PersistenceDiagrams has been accepted to @ieee_tvcg!
Available soon in the #TopologyToolKit :)
Details and experiments ๐
https://t.co/b69h78IdFO
#TopologicalDataAnalysis #visualization #machinelearning
๐ค The Wasserstein metric between #PersistenceDiagrams can be easily extended to #MergeTrees by restricting the set of candidate assignments to partial isomorphisms ๐
https://t.co/SNt8Tl6O5e
Available in the #TopologyToolKit!
#TopologicalDataAnalysis #DataScience #Visualization
๐ค The Wasserstein metric between #PersistenceDiagrams can be easily extended to #MergeTrees by restricting the set of candidate assignments to partial isomorphisms ๐
https://t.co/c2CXho32eu
Available in the #TopologyToolKit!
#TopologicalDataAnalysis #DataScience #Visualization
๐ข Need to analyze a collection of datasets based on their #MergeTrees?
Check out our new approach for Principal Geodesic Analysis in the Wasserstein space of #MergeTrees, with applications to dimension reduction ๐
https://t.co/SNt8Tl6gfG
#TopologicalDataAnalysis #DataScience
๐ค Given the L2-Wasserstein metric space between #MergeTrees (a), the reconstruction of a merge tree can be obtained by a sequence of displacements along geodesics which are parallel to the geodesic axes of the basis (a and g).
๐
https://t.co/SNt8Tl6gfG
#TopologicalDataAnalysis
๐ค Given the L2-Wasserstein metric space between #MergeTrees (a), the reconstruction of a merge tree can be obtained by a sequence of displacements along geodesics which are parallel to the geodesic axes of the basis (a and g).
๐
https://t.co/c2CXho2uoW
#TopologicalDataAnalysis
๐ค Given the L2-Wasserstein metric space between #MergeTrees with n persistence pairs (a), a pair of orthogonal geodesic axes can be represented as a pair of orthogonal vectors in R^(2n) (e). To be continued...
๐
https://t.co/SNt8Tl6gfG
#TopologicalDataAnalysis #TopologyToolKit
๐ค Given the L2-Wasserstein metric space between #MergeTrees with n persistence pairs (a), a geodesic axis is a pair of opposite collinear vectors in R^(2n) (d). To be continued...
๐
https://t.co/SNt8Tl6gfG
#TopologicalDataAnalysis #TopologyToolKit #Visualization #DataScience
๐ค Given the L2-Wasserstein metric space between #MergeTrees with n persistence pairs (a), a geodesic can be represented as a vector in R^(2n) (b). To be continued...
๐
https://t.co/SNt8TkOER6
#TopologicalDataAnalysis #TopologyToolKit #Visualization #DataScience #MachineLearning
Made a github gist to show persistence simplification of ordered #mergetrees of #sublevelset filtrations on #timeseries to incrementally remove of noise. Try it out yourself! #oddlysatisfying #tda #topology @matplotlib
https://t.co/HxTGOup6Pw
Checkout the new #TopologyToolKit Example Website!
Dozens of #TopologicalDataAnalysis pipelines.
Today, cluster a collection of datasets in the metric space of #MergeTrees in 107 lines of #Python!
#datascience #visualization
https://t.co/ko1ff6Pyjo
Need advanced #TopologicalDataAnalysis?
Go beyond #PersistenceDiagrams with #MergeTrees.
Improved feature tracking, temporal reduction, and clustering! Checkout our #ieeevis 2021 paper: https://t.co/3sFnKmutTs
An awesome teamwork :)
#DataScience #Visualization #MachineLearning
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