UMAP

Uniform Manifold Approximation and Projection (UMAP) is a powerful dimensionality reduction technique widely used in machine learning and data visualization. It operates by first learning the manifold structure of high-dimensional data, identifying relationships among data points through nearest neighbors. UMAP then projects this structure into a lower-dimensional space while preserving the essential features and distances of the original data. This method is particularly effective for visualizing complex datasets and can be applied in both unsupervised and supervised learning contexts. Its ability to maintain local and global data structures makes UMAP a popular choice among data scientists and researchers.

Biologists, stop putting UMAP plots in your papers

Simply Statistics

The UMAP craze in singe cell RNA-Seq Single-cell RNA sequencing (scRNA-seq) has become one of the most widely used technologies in basic biology. With the rise of scRNA-seq, the use of UMAP has becom...

UMAP Dimensionality Reduction — An Incredibly Robust Machine Learning Algorithm

Towards Data Science

How does Uniform Manifold Approximation and Projection (UMAP) work, and how to use it in Python

On the Validating UMAP Embeddings

Towards Data Science

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…

How to Analyze 100-Dimensional Data with UMAP in Breathtakingly Beautiful Ways

Towards Data Science

Learn to reduce dimensionality and visualize 100-dimensional datasets with UMAP by creating point clouds and connectivity plots and really "see" your data.

How to Use UMAP For Much Faster And Effective Outlier Detection

Towards Data Science

Let’s catch those high-dimensional outliers Continue reading on Towards Data Science

The AiEdge+: T-SNE and UMAP - Dimensionality Reduction

The AiEdge Newsletter

If you want to impress your friends at Data Science dinner parties with beautiful plots, t-SNE and UMAP are the way to go! These are significant dimensionality reduction techniques widely used in data...

Why you should not rely on t-SNE, UMAP or TriMAP

Towards Data Science

Dimensionality reduction techniques such as t-SNE¹, UMAP², and TriMap³ are ubiquitous within the field of data science, and given their impressive visual performance (combined with ease of use), they…...

Enum Map

Essential Java

Introduction Java EnumMap class is the specialized Map implementation for enum keys. It inherits Enum and AbstractMap classes. the Parameters for java.util.EnumMap class. K: It is the type of keys mai...

Implementation of Mean Average Precision (mAP) with Non-Maximum Suppression (NMS)

Towards Data Science

implementing NMS and mAP