2023年6月30日 · With UMAP, there are only ever the number of axes you choose (usually 2). Instead of these axes representing a pattern, the algorithm instead tries to choose locations of the points such that the distances in 2D match the “real” n-dimensional distances in the full data as closely as possible.

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 …

一种降维技术，假设可用数据样本均匀（Uniform）分布在拓扑空间（Manifold）中，可以从这些有限数据样本中近似（Approximation）并映射（Projection）到低维空间。 上面对算法的描述可能会对我们理解它的原理有一点帮助，但是对于UMAP是如何实现的仍然没有说清楚。 为了回答“如何”的问题，让我们分析UMAP执行的各个步骤。 我们可以将UMAP分为两个主要步骤: 找到该流形的低维表示。 下面我们将把它分解成更小的部分，以加深我们对算法的理解。 下面的地图显示 …

UMAP降维图本质也是散点图，只需要将作图数据导出，ggplot2就可以实现任何你想要的修饰了。 首先我们设置下颜色，并将作图的数据导出，导出的数据包含UMAP两个坐标的数据。

What is a UMAP? This is a special type of graph, called a Uniform Manifold Approximation and Projection (UMAP) . UMAPs are helpful ways of displaying many types of data and are often referred to as one type of dimensionality reduction tool.

2021年6月3日 · There probably are nice interpretations of UMAP, but it is not within linear algebra and requires a lot more advanced math as UMAP is based on Reimannian geometry. https://arxiv.org/abs/1802.03426. UMAP may have predictive value in some circumstances in ML, but building a predictive model is not the same as making inferences.

UMAP is a fairly flexible non-linear dimension reduction algorithm. It seeks to learn the manifold structure of your data and find a low dimensional embedding that preserves the essential topological structure of that manifold.

t-SNE （t-Distributed Stochastic Neighbor Embedding）和 UMAP （Uniform Manifold Approximation and Projection）是两种常用的降维方法，特别适用于单细胞数据的可视化和聚类。 t-SNE 和 UMAP 通常用于将基因表达数据转化为二维或三维的可视化图形。这些图形中的点代表细 …

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。 UMAP的主要目标是保持数据点之间的局部结构和全局结构。 它通过构建数据点之间的邻近关系图，并利用图的拓扑结构进行流形近似和优化。 UMAP使用了一种称为高维距离的度量方式，在低维空间中通过最小化原始距离和映射距离之间的差异来 …

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：