विधियों की तुलना करें

	MICE	ऋणात्मक मैट्रिक्स गुणनखंडन (NMF)
क्षेत्र	सांख्यिकी	मशीन अधिगम
परिवार	Process / pipeline	Latent structure
उद्भव वर्ष	2011	1999
प्रवर्तक	Stef van Buuren & Karin Groothuis-Oudshoorn	Lee, D. D. & Seung, H. S.
प्रकार	Iterative multiple imputation algorithm	Matrix decomposition with non-negativity constraints
मौलिक स्रोत	van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
उपनाम	Fully Conditional Specification, Sequential Regression Multivariate Imputation, Chained Equations Imputation, Zincirleme Denklemlerle Çoklu Atama	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
संबंधित	3	4
सारांश	Multivariate Imputation by Chained Equations (MICE) is an iterative procedure for handling missing data in multivariate datasets. Introduced by Stef van Buuren and Karin Groothuis-Oudshoorn through the R package mice (2011), the algorithm fills each missing variable using a separate regression model conditioned on all other variables, cycling through variables repeatedly until the imputed values converge. The result is m completed datasets that are analysed separately and combined using Rubin's rules.	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.