השוואת שיטות

	Isomap	PCA באמצעות גרעין (Kernel PCA)	ניתוח רכיבים עיקריים	t-SNE
תחום	למידת מכונה	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Latent structure	Latent structure	Machine learning	Machine learning
שנת המקור	2000	1998	2002	2008
הוגה השיטה	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	van der Maaten, L. & Hinton, G.
סוג	Manifold learning / nonlinear dimensionality reduction	Nonlinear dimensionality reduction via kernel trick	Unsupervised dimensionality reduction	Nonlinear dimensionality reduction (manifold visualization)
מקור מכונן	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
כינויים	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
קשורות	3	5	3	3
תקציר	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.	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.