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Feature-Based Rips Complex (Point Cloud to Simplicial Complex) #35
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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. |
Hello, @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 guillermo_bernardez@ucsb.edu so I can share access to the voting form. In your email, please include:
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. |
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:
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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