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Umap Top: tiny UMAP badge on boot prominent Citroen insignia on bonnet – UMAP hoped Citroen would take the bait Above: Dutch restored car bodywork for both its saloon and the short lived coupe So UMAP did not start from scratch

  • What makes a UMAP a good car?

    The Umap’s bodywork was noted for the quality of its fibreglass in its outer finish, and its fit and laminate uniformity which resulted in a body- complete weighing only 41kg. Inside the car the dashboard was a moulded design that swept around into the door caps on each side, making full use of the advantages of GRP.

  • Is Umap a non-linear dimension reduction technique?

    Similarly to most non-linear dimension re-duction techniques (including t-SNE and Isomap), UMAP lacks the strong interpretability of Principal Component Analysis (PCA) and related tech-niques such a Non-Negative Matrix Factorization (NMF).

  • What is a UMAP algorithm?

    UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance.

Umap vs t-SNE, at First Glance

Before diving into the theory behind UMAP, let's take a look at how it performs on real-world, high-dimensional data. The following visualization shows a comparison between using UMAP and t-SNE to project a subset of the 784-dimensional Fashion MNIST dataset down to 3 dimensions. Notice how well clustered each different category is (local structure

A Dip Into Umap Theory

UMAP, at its core, works very similarly to t-SNE - both use graph layout algorithms to arrange data in low-dimensional space. In the simplest sense, UMAP constructs a high dimensional graph representation of the data then optimizes a low-dimensional graph to be as structurally similar as possible. While the mathematics UMAP uses to construct the hi

Umap Parameters

By understanding the theory behind UMAP, it becomes much easier to understand the algorithm's parameters, especially compared with the perplexity parameter in t-SNE. We'll consider the two most commonly used parameters: n_neighbors and min_dist, which are effectively used to control the balance between local and global structure in the final projec

Umap vs t-SNE, Revisited

The biggest difference between the the output of UMAP when compared with t-SNE is this balance between local and global structure - UMAP is often better at preserving global structure in the final projection. This means that the inter-cluster relations are potentially more meaningful than in t-SNE. However, it's important to note that, because UMAP

How to (Mis)Read Umap

While UMAP offers a number of advantages over t-SNE, it's by no means a silver bullet - and reading and understanding its results requires some care. It's worth revisiting our previous work on (mis)reading t-SNE, since many of the same takeaways apply to UMAP: pair-code.github.io

Conclusion

UMAP is an incredibly powerful tool in the data scientist's arsenal, and offers a number of advantages over t-SNE. While both UMAP and t-SNE produce somewhat similar output, the increased speed, better preservation of global structure, and more understandable parameters make UMAP a more effective tool for visualizing high dimensional data. Finally,

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