Bandingkan kaedah

	Algoritma EM (Expectation-Maximization)	Penyelesaian Matriks
Bidang	Statistik	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1977	2009
Pengasas	Dempster, Laird & Rubin	Emmanuel Candès & Benjamin Recht
Jenis	Iterative optimization algorithm	Convex low-rank recovery
Sumber perintis	Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.	Candès, E. J., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6), 717–772.
Alias	EM, Expectation-Maximization, Maximum Likelihood via Incomplete Data, BM Algoritması	Nuclear Norm Minimization, Collaborative Filtering via Low-Rank Recovery, Inductive Matrix Completion, Matris Tamamlama
Berkaitan	2	2
Ringkasan	The Expectation-Maximization (EM) algorithm is an iterative optimization procedure for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with latent variables or missing data. Introduced by Dempster, Laird, and Rubin in their landmark 1977 paper, EM alternates between computing the expected complete-data log-likelihood (E-step) and maximizing it with respect to the parameters (M-step), guaranteeing monotone non-decreasing likelihood at each iteration.	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.