Comparar métodos

	Análisis de Componentes Independientes (ICA)	PCA con kernel
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Latent structure	Latent structure
Año de origen	1994	1998
Autor original	Comon, P.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
Tipo	Blind source separation / latent-structure decomposition	Nonlinear dimensionality reduction via kernel trick
Fuente seminal	Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.
Alias	ICA, blind source separation, BSS, FastICA	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
Relacionados	3	5
Resumen	Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive, statistically independent subcomponents. Formalized by Pierre Comon in 1994, ICA became the foundational framework for blind source separation and is widely applied in neuroimaging (fMRI, EEG), speech processing, and biomedical signal analysis.	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.