Summary of Towards One Model For Classical Dimensionality Reduction: a Probabilistic Perspective on Umap and T-sne, by Aditya Ravuri et al.
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
by Aditya Ravuri, Neil D. Lawrence
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 A medium-difficulty summary of the abstract: Dimensionality reduction methods UMAP and t-SNE are reinterpreted as MAP inference methods corresponding to a model called ProbDR, which estimates the data precision matrix using Wishart distributions. This interpretation provides deeper insights into these algorithms, highlighting potential misspecification of variances and connections to Gaussian process latent variable models via kernels describing graph Laplacian covariances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This paper takes a closer look at how dimensionality reduction methods work. It shows that some popular methods can be explained in a new way using a different model called ProbDR. This helps us understand these methods better and makes connections to other areas of research. The authors also provide tools for studying similar methods. |
Keywords
» Artificial intelligence » Dimensionality reduction » Inference » Precision » Umap
