https://github.com/jlmelville/jlmelville

My personal repository
https://github.com/jlmelville/jlmelville

Last synced: 27 days ago
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My personal repository

• Host: GitHub
• URL: https://github.com/jlmelville/jlmelville
• Owner: jlmelville
• Created: 2023-09-16T16:05:50.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-26T19:45:18.000Z (about 1 year ago)
• Last Synced: 2024-10-14T15:41:30.645Z (2 months ago)
• Size: 4.88 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
### Writing

I think about dimensionality reduction a lot, but currently have no need or urge to publish in the stick-a-PDF-on-arXiv sense. As a result, I have managed to accumulate various markdown documents and notebooks scattered across many repositiories. I am losing track of it all myself, so here I collect in one place some of the discussions, experiments and other musings.

- A comparison of the cost functions and gradients of t-SNE, LargeVis and UMAP:  and .

- LEOPOLD: an approximation to densMAP available in uwot: .

- Some recent explorations of dimensionality reduction, validation and visualization in Python using Jupyter notebooks: .

- Some Python notebooks that grab a variety of datasets that you might either enjoy running dimensionality reduction methods on, or just useful example code of how to use Python libraries to do some initial data reading and processing: . A more limited R package: .

- Graph Laplacians, Laplacian Eigenmaps, Diffusion maps and other spectral methods: .

- A discussion of using my nearest neighbor descent package to diagnose hubness in datasets and their affect on neighbor retrieval: .

- The smallvis doc page collects some of the above, plus discusses many other aspects of dimensionality reduction, especially t-SNE and variants: .

- How to derive the gradient of neighbor-embedding methods: .

- A (currently very out of date) bibliography of dimensionality reduction papers: .

- The different forms of Nesterov momentum as used in stochastic gradient descent methods: . Nothing much to do with dimensionality reduction, but you have to optimize those embeddings somehow.