Summary of Linear Partial Gromov-wasserstein Embedding, by Yikun Bai et al.
Linear Partial Gromov-Wasserstein Embedding
by Yikun Bai, Abihith Kothapalli, Hengrong Du, Rocio Diaz Martin, Soheil Kolouri
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Gromov Wasserstein (GW) problem, a variant of optimal transport (OT), has gained popularity in machine learning and data science due to its ability to quantify similarity between measures. However, both GW and its relaxed version, partial Gromov-Wasserstein (PGW), face computational challenges due to their non-convex nature. To overcome this, the linear partial Gromov-Wasserstein (LPGW) embedding is proposed, reducing the PGW problem’s computational complexity from O(K^2) to O(K). LPGW defines a valid metric for metric measure spaces and outperforms traditional OT-based methods in shape retrieval and learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about solving a math problem called Gromov Wasserstein. It helps compare different types of data. The problem has some difficulties, but the authors came up with a new way to solve it faster and better. This new method can be used in things like recognizing shapes and learning from data. |
Keywords
* Artificial intelligence * Embedding * Machine learning
