Võrdle meetodeid

	Singulaarspekteranalüüs	Independent Component Analysis (ICA)	Singular Value Decomposition
Valdkond	Aegread	Masinõpe	Numbrilised meetodid
Perekond	Process / pipeline	Latent structure	Machine learning
Tekkeaasta	1986	1994	1965
Looja	David Broomhead	Comon, P.	Gene Golub
Tüüp	Dimension reduction and trend extraction	Blind source separation / latent-structure decomposition	Linear algebra decomposition
Algallikas	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.	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.
Rööpnimetused	SSA, SVD-based decomposition	ICA, blind source separation, BSS, FastICA	SVD, thin SVD, reduced SVD
Seotud	3	3	0
Kokkuvõte	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.	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.