Latent structure
核主成分分析
核主成分分析（Kernel PCA）是由Bernhard Schölkopf、Alexander Smola和Klaus-Robert Müller在1997-1998年引入的一种非线性降维方法。它通过核函数隐式地将输入数据映射到一个高维特征空间，然后在该空间中执行标准PCA，从而将经典的线性PCA扩展到弯曲的、非线性数据流形上——所有这些操作都不需要显式计算映射。
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来源
- Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319. DOI: 10.1162/089976698300017467 ↗
- Schölkopf, B., Smola, A. J., & Müller, K.-R. (1997). Kernel principal component analysis. In Artificial Neural Networks — ICANN'97, Lecture Notes in Computer Science, Vol. 1327, pp. 583–588. Springer. DOI: 10.1007/BFb0020217 ↗
- Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press. ISBN: 978-0-262-19475-4
如何引用本页
ScholarGate. (2026, June 3). Kernel Principal Component Analysis. ScholarGate. https://scholargate.app/zh/machine-learning/kernel-pca
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