Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kodola PCA× | Isomap× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1998 | 2000 |
| Autors≠ | Schölkopf, B.; Smola, A. J.; Müller, K.-R. | Tenenbaum, J. B.; de Silva, V.; Langford, J. C. |
| Tips≠ | Nonlinear dimensionality reduction via kernel trick | Manifold learning / nonlinear dimensionality reduction |
| Pirmavots≠ | Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319. DOI ↗ | Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323. DOI ↗ |
| Citi nosaukumi | KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition | Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. | Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system. |
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