Summary of Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-wasserstein, by Hugues Van Assel et al.
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
by Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer
First submitted to arxiv on: 3 Feb 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 introduces a new framework called distributional reduction that combines dimensionality reduction (DR) methods with clustering techniques using optimal transport. This framework allows for joint optimization of DR and clustering tasks, enabling the identification of low-dimensional prototypes representing data at different scales. The authors demonstrate the relevance of this approach across multiple image and genomic datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding patterns in big data without labeling it first. It’s like trying to find shapes in a messy picture. Right now, we have two ways to do this: make the picture smaller (dimensionality reduction) or group similar things together (clustering). This new method combines these two approaches and helps us find the most important features of the data. |
Keywords
* Artificial intelligence * Clustering * Dimensionality reduction * Optimization
