Latent structure
Isomap
Isomap（等距特征映射）是一种流形学习算法，由 Tenenbaum、de Silva 和 Langford 于 2000 年提出，它通过保留所有点对之间测地线（而非直线欧几里得）距离来发现高维数据的内在低维几何结构。它是最早也是最具影响力的非线性降维方法之一，证明了真正弯曲的数据流形可以被展开成一个忠实的低维坐标系。
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来源
- Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323. DOI: 10.1126/science.290.5500.2319 ↗
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer. ISBN: 978-0-387-84857-0
- van der Maaten, L., Postma, E. & van den Herik, J. (2009). Dimensionality reduction: A comparative review. Journal of Machine Learning Research, 10, 66–71. link ↗
如何引用本页
ScholarGate. (2026, June 3). Isometric Feature Mapping (Isomap). ScholarGate. https://scholargate.app/zh/machine-learning/isomap
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