เปรียบเทียบวิธี

	การเติมเต็มเมทริกซ์	MICE
สาขาวิชา	การเรียนรู้ของเครื่อง	สถิติศาสตร์
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	2009	2011
ผู้ริเริ่ม	Emmanuel Candès & Benjamin Recht	Stef van Buuren & Karin Groothuis-Oudshoorn
ประเภท	Convex low-rank recovery	Iterative multiple imputation algorithm
แหล่งต้นตำรับ	Candès, E. J., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6), 717–772.	van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
ชื่อเรียกอื่น	Nuclear Norm Minimization, Collaborative Filtering via Low-Rank Recovery, Inductive Matrix Completion, Matris Tamamlama	Fully Conditional Specification, Sequential Regression Multivariate Imputation, Chained Equations Imputation, Zincirleme Denklemlerle Çoklu Atama
ที่เกี่ยวข้อง	2	3
สรุป	Matrix Completion is a technique for recovering a low-rank matrix from a small, possibly random subset of its entries. Introduced by Emmanuel Candès and Benjamin Recht in 2009, it reformulates the problem as nuclear norm minimization — a convex surrogate for rank minimization — and provides theoretical guarantees that exact recovery is achievable when entries are observed uniformly at random and the matrix satisfies an incoherence condition.	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.