Uporedite metode

	Независна компонента анализа (ICA)	Kernel PCA
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Latent structure	Latent structure
Godina nastanka	1994	1998
Tvorac	Comon, P.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
Tip	Blind source separation / latent-structure decomposition	Nonlinear dimensionality reduction via kernel trick
Temeljni izvor	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.
Drugi nazivi	ICA, blind source separation, BSS, FastICA	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
Srodne	3	5
Sažetak	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.