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Understanding UMAP\n\nDimensionality reduction is a powerful tool for machine learning practitioners to visualize and understand large, high dimensional datasets. One of the most widely used techniques for visualization is [t-SNE](https://lvdmaaten.github.io/tsne/), but its performance suffers with large datasets and using it correctly can be [challenging](https://distill.pub/2016/misread-tsne/).\n\n[UMAP](https://github.com/lmcinnes/umap) is a new technique by McInnes et al. that offers a number of advantages over t-SNE, most notably increased speed and better preservation of the data's global structure. In this article, we'll take a look at the theory behind UMAP in order to better understand how the algorithm works, how to use it effectively, and how its performance compares with t-SNE.\n\n```bash\nyarn\nyarn dev\n```\n\n#### Publishing to github pages\n\n```bash\nyarn pub\n```\n\n#### To develop figures individually\n\n```bash\nyarn dev:cech\nyarn dev:hyperparameters\nyarn dev:mammoth-umap\nyarn dev:mammoth-tsne\nyarn dev:supplement\nyarn dev:toy\nyarn dev:toy_comparison\n```\n\n#### Data preprocessing\n\nFor the mammoth figures, the [raw 3D data](https://github.com/MNoichl/UMAP-examples-mammoth-/blob/master/mammoth_a.csv) was downsampled to 50,000 points before being projected with UMAP / t-SNE. These 50,000 points were then randomly subsampled to 10,000 points in order to minimize the payload size.\n\n_Understanding UMAP_ uses a few tricks to make the data payloads for some of the interactive figures small enough to download in a reasonable time. The `mammoth` figures use a 10-bit encoding scheme to compress the 10,000 data points into a significantly smaller payload. The `hyperparameters` and `toy_comparison` figures precompute UMAP embeddings for all of their different combinations, then use the same 10-bit encoding scheme to compress the data.\n\n```bash\nyarn preprocess:hyperparameters\nyarn preprocess:mammoth\nyarn preprocess:toy_comparison\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpair-code%2Funderstanding-umap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpair-code%2Funderstanding-umap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpair-code%2Funderstanding-umap/lists"}