Сравнение методов

	Завершение матрицы	Неотрицательное матричное разложение (NMF)
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Latent structure
Год появления	2009	1999
Автор метода	Emmanuel Candès & Benjamin Recht	Lee, D. D. & Seung, H. S.
Тип	Convex low-rank recovery	Matrix decomposition with non-negativity constraints
Основополагающий источник	Candès, E. J., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6), 717–772.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Другие названия	Nuclear Norm Minimization, Collaborative Filtering via Low-Rank Recovery, Inductive Matrix Completion, Matris Tamamlama	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Связанные	2	4
Сводка	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.	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.