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半监督K近邻

半监督K近邻（Semi-supervised KNN）算法是对经典的K近邻算法的扩展，它能够利用大量未标记数据和少量标记数据。通过在所有观测数据上构建K近邻图，并沿着图的边传播已知标签，该方法可以在无需昂贵的手动标注每个样本的情况下推断出未标记点的标签。

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Method map

The neighbourhood of related methods — select a node to explore.

半监督K近邻
标签传播半监督高斯过程半监督学习半监督支持向量机主动学习 K-近邻在线K近邻自监督 K-近邻

来源

  1. Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗
  2. Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9

如何引用本页

ScholarGate. (2026, June 3). Semi-supervised K-Nearest Neighbors (Label Propagation via KNN Graph). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-k-nearest-neighbors

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.

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被引用于

主动学习 K-近邻在线K近邻自监督 K-近邻

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ScholarGate — Semi-supervised K-nearest neighbors (Semi-supervised K-Nearest Neighbors (Label Propagation via KNN Graph)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/semi-supervised-k-nearest-neighbors · 数据集： https://doi.org/10.5281/zenodo.20539026