Machine learning
UMAP
UMAP（Uniform Manifold Approximation and Projection，均匀流形逼近与投影）是一种快速、可扩展的非线性降维方法，其理论基础是流形学习，由 McInnes、Healy 和 Melville 于 2018 年提出。它将高维数据压缩成低维嵌入，用于可视化和后续分析。
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来源
- McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426. link ↗
如何引用本页
ScholarGate. (2026, June 1). Uniform Manifold Approximation and Projection. ScholarGate. https://scholargate.app/zh/machine-learning/umap-reduction
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