Bandingkan kaedah

	Analisis Spektrum Singular	Analisis Komponen Bebas (ICA)	Penguraian Nilai Tunggal
Bidang	Siri Masa	Pembelajaran Mesin	Kaedah Berangka
Keluarga	Process / pipeline	Latent structure	Machine learning
Tahun asal	1986	1994	1965
Pengasas	David Broomhead	Comon, P.	Gene Golub
Jenis	Dimension reduction and trend extraction	Blind source separation / latent-structure decomposition	Linear algebra decomposition
Sumber perintis	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.
Alias	SSA, SVD-based decomposition	ICA, blind source separation, BSS, FastICA	SVD, thin SVD, reduced SVD
Berkaitan	3	3	0
Ringkasan	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.