This is a tutorial of using UMAP to embed text (but this can be extended to any collection of tokens). We are going to use the 20 newsgroups dataset which is a collection of forum posts labelled by topic.

2025年1月9日 · UMAP 图，全称是 统一流形逼近与投影图，是数据降维可视化的神器 它能把复杂的高维数据，巧妙地投影到二维或三维空间，让我们一眼看清数据分布与关系。

2023年9月16日 · UMAP是一种非线性降维和可视化算法，全称为Uniform Manifold Approximation and Projection（均匀流形近似和投影）。 它是一种基于图论和流形学习的方法，用于将高维数据映射到低维空间，以便于可视化和分析。

2021年1月9日 · In this tutorial, we will take the embedding extracted from COCO pictures using the ResNext-WSL model, the sparse topic representation provided by the UMAP transformation, the GMM clustering model, and we will produce an embedding representation for collections of pictures (Bag Of Words documents).

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. …

2024年11月21日 · Uniform manifold approximation and projection (UMAP) is a nonlinear dimension reduction method often used for visualizing data and as pre-processing for further...

This is a tutorial of using UMAP to embed text (but this can be extended to any collection of tokens). We are going to use the 20 newsgroups dataset which is a collection of forum posts labelled by topic. We are going to embed these documents and see that similar documents (i.e. posts in the same subforum) will end up close together.

2024年1月23日 · UMAP（Uniform Manifold Approximation and Projection for Dimension Reduction）是一种非线性降维技术，类似于t-SNE、PCA，可用于可视化。 在降维应用中， 相比于t-SNE，umap既快又准。 如果对 UMAP算法感兴趣，可以阅读论文. McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018.

UMAP is a useful tool for general exploratory analysis of data – it can provide a unique lens through which to view data that can highlight structures and properties hiding in data that are not as apparent when analysed with other techniques.

You can use this embedding for other downstream tasks such as visualizing your corpus or run a clustering algorithm (e.g. HDBSCAN). We will use a bag of words model and use UMAP on the count vectors as well as the TF-IDF vectors.