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| Kernel PCA× | Support Vector Machine (Klassifikation)× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie≠ | Latent structure | Machine learning |
| Entstehungsjahr≠ | 1998 | 1995 |
| Urheber≠ | Schölkopf, B.; Smola, A. J.; Müller, K.-R. | Cortes, C. & Vapnik, V. |
| Typ≠ | Nonlinear dimensionality reduction via kernel trick | Maximum-margin classifier (kernel method) |
| Wegweisende Quelle≠ | Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Aliasnamen | KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
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