嵌入
数学
内在维度
投影(关系代数)
计算机科学
维数(图论)
多维标度
歧管(流体力学)
可视化
降维
可扩展性
黎曼流形
还原(数学)
复杂尺寸
拓扑(电路)
算法
非线性降维
纯数学
人工智能
几何学
组合数学
机器学习
机械工程
数据库
维数之咒
工程类
作者
Leland McInnes,John J. Healy
出处
期刊:Cornell University - arXiv
日期:2018-02-09
被引量:645
摘要
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
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