Dimensionality 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, but its performance suffers with large datasets and using it correctly can be challenging.
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.
yarn
yarn devyarn pubyarn dev:cech
yarn dev:hyperparameters
yarn dev:mammoth-umap
yarn dev:mammoth-tsne
yarn dev:supplement
yarn dev:toy
yarn dev:toy_comparisonFor the mammoth figures, the raw 3D data 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.
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.
yarn preprocess:hyperparameters
yarn preprocess:mammoth
yarn preprocess:toy_comparison