विधियों की तुलना करें

	स्वतंत्र घटक विश्लेषण (ICA)	कर्नेल पीसीए	एकवचन मान अपघटन
क्षेत्र	मशीन अधिगम	मशीन अधिगम	संख्यात्मक विधियाँ
परिवार	Latent structure	Latent structure	Machine learning
उद्भव वर्ष	1994	1998	1965
प्रवर्तक	Comon, P.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Gene Golub
प्रकार	Blind source separation / latent-structure decomposition	Nonlinear dimensionality reduction via kernel trick	Linear algebra decomposition
मौलिक स्रोत	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.
उपनाम	ICA, blind source separation, BSS, FastICA	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	SVD, thin SVD, reduced SVD
संबंधित	3	5	0
सारांश	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.