Machine learning
局部线性嵌入 (LLE)
局部线性嵌入（Locally Linear Embedding，简称LLE）由Sam Roweis和Lawrence Saul于2000年提出，是一种用于非线性降维的流形学习方法。它假设数据虽然可能在高维空间中弯曲，但每个点及其邻域近似位于一个平面区域上。LLE将每个点表示为其邻居的加权组合，然后找到一个低维布局，该布局保留了这些相同的局部关系，从而将弯曲结构展开成忠实的低维映射。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. DOI: 10.1126/science.290.5500.2323 ↗
如何引用本页
ScholarGate. (2026, June 2). Locally Linear Embedding (LLE). ScholarGate. https://scholargate.app/zh/machine-learning/locally-linear-embedding
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →