gasteigerjo/lcn
Locally corrected Nyström (LCN), as proposed in "Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More" (ICML 2021)
This project helps researchers and data scientists efficiently compare complex data structures, like sets of high-dimensional data points or graphs. You provide your data (e.g., word embeddings or graph representations), and it outputs a distance measure or a 'transport plan' showing how to map one dataset to another. It's designed for those who need to measure similarity or align embeddings at scale.
No commits in the last 6 months.
Use this if you need to perform optimal transport calculations for large, high-dimensional datasets or graph structures, and require fast, scalable methods.
Not ideal if your data is low-dimensional, or if you primarily need to compare individual data points rather than entire sets or distributions.
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19
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3
Language
Python
License
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Last pushed
Apr 26, 2023
Commits (30d)
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