Summary of Tree-wasserstein Distance For High Dimensional Data with a Latent Feature Hierarchy, by Ya-wei Eileen Lin et al.
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
by Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper proposes a new tree-Wasserstein distance (TWD) for high-dimensional data with a latent feature hierarchy. The TWD is designed to learn the hierarchical structure of features rather than embedding samples in hyperbolic space, unlike traditional approaches. The method uses diffusion geometry to embed features into a multi-scale hyperbolic space and then applies a tree decoding technique. The authors demonstrate that their TWD can efficiently recover the latent feature hierarchy and show its scalability on word-document and single-cell RNA-sequencing datasets. This approach outperforms existing methods based on pre-trained models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to measure distances between high-dimensional data points, which is useful for learning about how features are related. It’s like building a map of how different things are connected. The method uses special math to help it understand the relationships between these features and then finds a way to use those connections to predict how data will look in the future. The results show that this new approach can be very useful for analyzing words, documents, and even tiny cells. |
Keywords
» Artificial intelligence » Diffusion » Embedding
