Summary of Out-of-core Dimensionality Reduction For Large Data Via Out-of-sample Extensions, by Luca Reichmann et al.
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions
by Luca Reichmann, David Hägele, Daniel Weiskopf
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper aims to improve dimensionality reduction (DR) methods for high-dimensional datasets by introducing out-of-sample extensions. This allows for the projection of new data into existing projections, making it possible to perform DR on large datasets that would otherwise be memory- and runtime-intensive. The authors contribute an implementation of metric multidimensional scaling (MDS) with out-of-sample projection capability and evaluate the quality of five common DR algorithms (MDS, PCA, t-SNE, UMAP, and autoencoders) using various metrics. They also analyze the trade-off between reference set size and projection quality, as well as the runtime behavior of the algorithms. Additionally, the authors compare their out-of-sample approach to other recently introduced DR methods, such as PaCMAP and TriMAP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a way to make dimensionality reduction (DR) work with really big datasets. Currently, DR is only used for small datasets because it takes up too much memory and time. The authors came up with an idea called out-of-sample extensions that lets you add new data to existing projections. This makes it possible to do DR on huge datasets that would otherwise be impossible. They tested five different DR methods (MDS, PCA, t-SNE, UMAP, and autoencoders) and found the best way to balance the size of the reference set with the quality of the projection. They also compared their method to other new ways of doing DR. |
Keywords
» Artificial intelligence » Dimensionality reduction » Pca » Umap
