Uniform Manifold Approximation and Projection (UMAP)  Python Automation and Machine Learning for ICs   An Online Book  

Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= Uniform Manifold Approximation and Projection (UMAP) was introduced in a 2018 paper by McInnes and Healy. UMAP is a machine learning technique used for dimensionality reduction and nonlinear manifold learning. It is particularly effective for visualizing highdimensional data in lower dimensions. UMAP operates by representing highdimensional data in a lowerdimensional space while preserving the local structure of the data as much as possible. Unlike traditional techniques like Principal Component Analysis (PCA), which focuses on preserving global structure, UMAP aims to conserve both global and local structures. This makes it particularly useful for preserving complex nonlinear relationships present in many realworld datasets. UMAP works by constructing a highdimensional graph representation of the data, where data points are connected to their nearest neighbors. It then optimizes a lowdimensional representation of this graph such that the lowdimensional points maintain similar local relationships as the highdimensional points. The optimization process involves minimizing a cost function that balances the preservation of local neighborhoods with minimizing the overall energy of the system. One of the key advantages of UMAP is its scalability and efficiency, allowing it to handle large datasets with millions of points. It has become widely used in fields such as machine learning, data analysis, and visualization for tasks such as clustering, visualization, and feature extraction.
============================================


=================================================================================  

