Summary of Curvature Augmented Manifold Embedding and Learning, by Yongming Liu
Curvature Augmented Manifold Embedding and Learning
by Yongming Liu
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 proposed Curvature-Augmented Manifold Embedding and Learning (CAMEL) method is a novel dimensional reduction (DR) and data visualization approach that formulates the DR problem as a mechanistic/physics model. This unique methodology includes a non-pairwise force, inspired by lattice-particle physics and Riemann curvature in topology. CAMEL is applied to various benchmark datasets, including tSNE, UMAP, TRIMAP, and PacMap, with both visual comparison and metrics-based evaluation performed using 14 open literature and self-proposed metrics. The method demonstrates promising results for unsupervised learning, supervised learning, semi-supervised learning/metric learning, and inverse learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAMEL is a new way to show and understand big data. It’s like a map that helps us find patterns in really big collections of numbers. This approach uses ideas from physics to make the map more accurate and useful. The creators tested CAMEL on many different datasets and showed that it can be used for things like finding clusters, making predictions, and learning relationships between variables. |
Keywords
* Artificial intelligence * Embedding * Semi supervised * Supervised * Tsne * Umap * Unsupervised
