Kernel PCA
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.
Rekod sumber
Petikan disalin secara verbatim daripada rekod sumber kaedah. Tiada pengesahan peringkat tuntutan disimpulkan daripadanya.
- 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
Tuntutan yang dikurasi
Tuntutan disimpan dalam lejar bukti, setiap satu dengan penilaiannya sendiri.
Pandangan ini tidak mencipta penilaian tuntutan apabila lejar tiada.
Kaedah berkaitan
Dijana daripada graf kaedah dan ditunjukkan sebagai perhubungan yang dicadangkan mesin — tiada tuntutan bukti disimpulkan.