Machine learning
谱聚类
谱聚类（Spectral Clustering）是一种基于图的无监督学习算法，由 Ng、Jordan 和 Weiss 于 2002 年正式提出。该算法首先将数据点映射到一个低维的特征空间，该空间由相似性图的拉普拉斯矩阵导出，然后再应用 k-means 算法。这种谱嵌入使得恢复任意形状的簇（如环状、新月形、交织螺旋形）成为可能，而基于欧氏距离的方法在分离这些簇时常常会失败。
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来源
- Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856. link ↗
- von Luxburg, U. (2007). A Tutorial on Spectral Clustering. Statistics and Computing, 17, 395–416. DOI: 10.1007/s11222-007-9033-z ↗
- Shi, J., & Malik, J. (2000). Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905. DOI: 10.1109/34.868688 ↗
如何引用本页
ScholarGate. (2026, June 3). Spectral Clustering via Graph Laplacian Eigenvectors (Ng–Jordan–Weiss Algorithm). ScholarGate. https://scholargate.app/zh/machine-learning/spectral-clustering
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