방법 비교

	독립 성분 분석 (ICA)	음이 아닌 행렬 분해(NMF)
분야	머신러닝	머신러닝
계열	Latent structure	Latent structure
기원 연도	1994	1999
창시자	Comon, P.	Lee, D. D. & Seung, H. S.
유형	Blind source separation / latent-structure decomposition	Matrix decomposition with non-negativity constraints
원전	Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
별칭	ICA, blind source separation, BSS, FastICA	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
관련	3	4
요약	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.	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.