Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data. The data is uniformly distributed on Riemannian manifold;

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 ...

UMAP is a general purpose manifold learning and dimension reduction algorithm. It is designed to be compatible with scikit-learn, making use of the same API and able to be added to sklearn pipelines. If you are already familiar with sklearn you should be able to use UMAP as a drop in replacement for t-SNE and other dimension reduction classes.

本文将介绍一种流行的降维技术Uniform Manifold Approximation and Projection (UMAP)的内部工作原理，并提供一个 Python 示例。 (UMAP) 如何工作的？ 分析 UMAP 名称. 让我们从剖析 UMAP 名称开始，这将使我们对算法应该做什么有一个大致的了解。 以下描述不是官方定义，而是我总结出来的可帮助我们理解 UMAP 的要点。 Projection ——通过投影点在平面、曲面或线上再现空间对象的过程或技术。 也可以将其视为对象从高维空间到低维空间的映射。 Approximation——算 …

UMAP is an algorithm for dimension reduction based on manifold learning techniques and ideas from topological data analysis. It provides a very general framework for approaching manifold learning and dimension reduction, but can also provide specific concrete realizations. This article will discuss how the algorithm works in practice.

UMAP 全称是：Uniform Manifold Approximation and Projection，中文可以叫做：统一流形近似与投影。 Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high.

2024年11月21日 · In this Primer, we provide an introduction to the UMAP algorithm, how it works, how best to apply it on data and how to interpret and understand results. Our focus is on providing a starting...

2024年7月3日 · Uniform Manifold Approximation and Projection (UMAP) is a powerful dimension reduction technique that has gained significant traction in the fields of machine learning and data visualization. Developed by Leland McInnes, John Healy, and James Melville, UMAP is built on solid mathematical foundations, including Riemannian geometry and algebraic ...

什么是umap？和pca一样，一种降维的算法，如果不是统计学或者数据专业的人，我建议不要去看它的原理，知道如何用就足够了。 也许听到umap最多的是对单细胞数据的分析降维，类似于下图： 然而其他数据，像大样本的…

2024年5月24日 · Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are among the tools life scientists turn to for...