Vertaile menetelmiä

	Riippumattomien komponenttien analyysi (ICA)	Kernel PCA
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Latent structure	Latent structure
Syntyvuosi	1994	1998
Kehittäjä	Comon, P.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
Tyyppi	Blind source separation / latent-structure decomposition	Nonlinear dimensionality reduction via kernel trick
Alkuperäislähde	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.
Rinnakkaisnimet	ICA, blind source separation, BSS, FastICA	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
Liittyvät	3	5
Tiivistelmä	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.