| First Authors | Luis Scoccola |
|---|---|
| Authors | Luis Scoccola, Uzu Lim, Heather A Harrington |
| Corresponding Authors | Luis Scoccola |
| Last Authors | Heather A Harrington |
| Conference Proceedings Volume Title | Proceedings of the 42nd International Conference on Machine Learning |
| Series Title | (Proceedings of Machine Learning Research ; 267) |
| Conference Name | 42nd International Conference on Machine Learning - ICML-Annual |
| Conference Location | Vancouver, CANADA |
| Conference Start Date | 2025-06-13 |
| Conference End Date | 2025-06-19 |
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| Publisher | JMLR-Journal Machine Learning Research |
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| ISBN | |
| First Page | 53728 |
| Last Page | 53756 |
| Open Access | true |
| Print Publication Date | 2025-06-19 |
| Online Publication Date | 2025-06-19 |
| Abstract | Classical unsupervised learning methods like clustering and linear dimensionality reduction parametrize large-scale geometry when it is discrete or linear, while more modern methods from manifold learning find low dimensional representation or infer local geometry by constructing a graph on the input data. More recently, topological data analysis popularized the use of simplicial complexes to represent data topology with two main methodologies: topological inference with geometric complexes and large-scale topology visualization with Mapper graphs - central to these is the nerve construction from topology, which builds a simplicial complex given a cover of a space by subsets. While successful, these have limitations: geometric complexes scale poorly with data size, and Mapper graphs can be hard to tune and only contain low dimensional information. In this paper, we propose to study the problem of learning covers in its own right, and from the perspective of optimization. We describe a method for learning topologically-faithful covers of geometric datasets, and show that the simplicial complexes thus obtained can outperform standard topological inference approaches in terms of size, and Mapper-type algorithms in terms of representation of large-scale topology. |
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| Affiliated With | CSBD, Harrington |
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| Publication Status | Published |
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| WebOfScience Link | WOS:001693164800033 |
| Alternative Full Text URL | https://proceedings.mlr.press/v267/ |
| Display Publisher Download Only | false |
| Visible On MPI-CBG Website | true |
| PDF Downloadable | true |
| Created By | thuem |
| Added Date | 2026-04-16 |
| Last Edited By | thuem |
| Last Edited Date | 2026-04-24 13:38:18.9 |
| Library ID | 9207 |
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| Entry Complete | true |
| eDoc Compliant | true |
| Include in Edoc Report | true |
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| Ready for eDoc Export | false |
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