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This is a generalization of the usual Rips-Complex, where the pairwise distances can include a factor based on the Euclidean distance of the feature vectors. This may be useful in cases where the features give additional information about the underlying topology (e.g. two points appear close when embedded in Euclidean space but are far apart on the manifold). It can also be used to create models that have more expressive message-passing dynamics, with message passing being done over both the position-based topology and the feature-based topology.
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Hi,
Information for my team is below:
Name: Theodore Long Method: Feature-Based Rips Complex Lifting Input Domain: Point Cloud Output Domain: Simplicial Complex PR Number: 35
Thanks, Theo
On Jul 8, 2024, at 12:46 PM, levtelyatnikov @.***> wrote:
Hello, @theo-long https://github.com/theo-long ! Thank you for your submission. As we near the end of the challenge, I am collecting participant info for the purpose of selecting and announcing winners. Please email me (or have one member of your team email me) at @. @.> so I can share access to the voting form. In your email, please include: • your first and last name (as well as any other team members) • the title of the method you implemented • the input domain of the method you implemented • the output domain of the method you implemented • your pull request number (#35 https://github.com/pyt-team/challenge-icml-2024/pull/35) Before July 12, make sure that your submission respects all Submission Requirements laid out on the challenge page. Any submission that fails to meet this criteria will be automatically disqualified.
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Implements a feature-based version of the vietoris-rips complex, which allows one to interpolate linearly between 0.0 = standard rips complex and 1.0 = rips complex based entirely on euclidean distance between feature vectors