#umap #trustworthiness #geometry
https://towardsdatascience.com/on-the-validating-umap-embeddings-2c8907588175
https://towardsdatascience.com/on-the-validating-umap-embeddings-2c8907588175
Medium
On the Validation of UMAP
There is not a large body of practical work on validating Uniform Manifold Approximation and Projection (UMAP). In this blog post, I will show you a real example, in hopes to provide an additional…
👍1
#umap #tsne #dimreducers #manifold
Понравилась интерактивная визуализация кластеров датасета одежды. Ну и мамонт, конечно.
"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, it's important to remember that no dimensionality reduction technique is perfect - by necessity, we're distorting the data to fit it into lower dimensions - and UMAP is no exception.
However, by building up an intuitive understanding of how the algorithm works and understanding how to tune its parameters, we can more effectively use this powerful tool to visualize and understand large, high-dimensional datasets."
https://pair-code.github.io/understanding-umap/
Понравилась интерактивная визуализация кластеров датасета одежды. Ну и мамонт, конечно.
"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, it's important to remember that no dimensionality reduction technique is perfect - by necessity, we're distorting the data to fit it into lower dimensions - and UMAP is no exception.
However, by building up an intuitive understanding of how the algorithm works and understanding how to tune its parameters, we can more effectively use this powerful tool to visualize and understand large, high-dimensional datasets."
https://pair-code.github.io/understanding-umap/