Salīdzināt metodes

	Singulārā spektra analīze	Neatkarīgo komponentu analīze (ICA)	Kodola PCA	Singular Value Decomposition
Nozare	Laikrindas	Mašīnmācīšanās	Mašīnmācīšanās	Skaitliskās metodes
Saime	Process / pipeline	Latent structure	Latent structure	Machine learning
Izcelsmes gads	1986	1994	1998	1965
Autors	David Broomhead	Comon, P.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Gene Golub
Tips	Dimension reduction and trend extraction	Blind source separation / latent-structure decomposition	Nonlinear dimensionality reduction via kernel trick	Linear algebra decomposition
Pirmavots	Broomhead, D. S., & King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2–3), 217–236.	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.	Golub, G. H., & Kahan, W. (1970). Calculating the singular values and pseudo-inverse of a matrix. Journal of the SIAM Series B: Numerical Analysis, 2(2), 205–224.
Citi nosaukumi	SSA, SVD-based decomposition	ICA, blind source separation, BSS, FastICA	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	SVD, thin SVD, reduced SVD
Saistītās	3	3	5	0
Kopsavilkums	Singular Spectrum Analysis (SSA) is a nonparametric method for time-series decomposition and forecasting based on singular value decomposition (SVD) of a time-lagged embedding matrix. Introduced by Broomhead and King (1986) and developed further by Vautard, Yiou, and Ghil (1992), SSA decomposes time series into trend, oscillatory, and noise components without assuming any underlying model. It is particularly effective for short, noisy non-stationary signals where parametric approaches fail.	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.	Singular Value Decomposition (SVD) is a fundamental matrix factorization technique that decomposes any m × n matrix A into the product A = U Σ V^T, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values. Developed by Gene Golub and others in the 1960s–1970s, SVD is the most robust method for analyzing matrix structure and solving linear systems.