• Corpus ID: 237371746

Stitch Fix for Mapper and Topological Gains

@inproceedings{Zhou2021StitchFF,
  title={Stitch Fix for Mapper and Topological Gains},
  author={Youjia Zhou and Nathaniel Saul and Ilkin Safarli and Bala Krishnamoorthy and Bei Wang},
  year={2021}
}
The mapper construction is a powerful tool from topological data analysis that is designed for the analysis and visualization of multivariate data. In this paper, we investigate a method for stitching a pair of univariate mappers together into a bivariate mapper, and study topological notions of information gains, referred to as topological gains, during such a process. We further provide implementations that visualize such topological gains for mapper graphs. 
1 Citations
Adaptive Covers for Mapper Graphs Using Information Criteria
  • N. Chalapathi, Youjia Zhou, Bei Wang
  • 2021 IEEE International Conference on Big Data (Big Data)
  • 2021
The mapper construction is a widely used tool from topological data analysis in obtaining topological summaries of large, high-dimensional point cloud data. It has enjoyed great success in data

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