Comparar métodos

	PCA con kernel	t-SNE
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Latent structure	Machine learning
Año de origen	1998	2008
Autor original	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	van der Maaten, L. & Hinton, G.
Tipo	Nonlinear dimensionality reduction via kernel trick	Nonlinear dimensionality reduction (manifold visualization)
Fuente seminal	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
Alias	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
Relacionados	5	3
Resumen	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.	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.