Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Ανάλυση Κύριων Συνιστωσών με Πυρήνα (Kernel PCA)× | Ανάλυση Ιδιαζουσών Τιμών× | |
|---|---|---|
| Πεδίο≠ | Μηχανική Μάθηση | Αριθμητικές Μέθοδοι |
| Οικογένεια≠ | Latent structure | Machine learning |
| Έτος προέλευσης≠ | 1998 | 1965 |
| Δημιουργός≠ | Schölkopf, B.; Smola, A. J.; Müller, K.-R. | Gene Golub |
| Τύπος≠ | Nonlinear dimensionality reduction via kernel trick | Linear algebra decomposition |
| Θεμελιώδης πηγή≠ | Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319. DOI ↗ | Golub, G. H., & Kahan, W. (1970). Calculating the singular values and pseudo-inverse of a matrix. Journal of the SIAM Series B: Numerical Analysis, 2(2), 205–224. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition | SVD, thin SVD, reduced SVD |
| Συναφείς≠ | 5 | 0 |
| Σύνοψη≠ | 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. | Singular Value Decomposition (SVD) is a fundamental matrix factorization technique that decomposes any m × n matrix A into the product A = U Σ V^T, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values. Developed by Gene Golub and others in the 1960s–1970s, SVD is the most robust method for analyzing matrix structure and solving linear systems. |
| ScholarGateΣύνολο δεδομένων ↗ |
|
|