Low-Rank Matrix Factorization Algorithm: Difference between revisions
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A [[Low-Rank Matrix Factorization Algorithm]] is a [[matrix factorization algorithm]] that can be implemented by a [[low-rank matrix factorization system]] (which solves a [[low-rank matrix factorization task]] intended to compress a [[large matrix]] into a [[low-rank matric]]es). | A [[Low-Rank Matrix Factorization Algorithm]] is a [[matrix factorization algorithm]] that can be implemented by a [[low-rank matrix factorization system]] (which solves a [[low-rank matrix factorization task]] intended to compress a [[large matrix]] into a [[low-rank matric]]es). | ||
* <B>See:</B> [[SVD Algorithm]], [[Bayesian Inference]], [[MCMC Method]], [[Bayesian Personalized Ranking Algorithm]]. | * <B>See:</B> [[SVD Algorithm]], [[Bayesian Inference]], [[MCMC Method]], [[Bayesian Personalized Ranking Algorithm]]. | ||
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Latest revision as of 21:27, 17 September 2021
A Low-Rank Matrix Factorization Algorithm is a matrix factorization algorithm that can be implemented by a low-rank matrix factorization system (which solves a low-rank matrix factorization task intended to compress a large matrix into a low-rank matrices).
References
2008
- (Salakhutdinov & Mnih, 2008) ⇒ Ruslan Salakhutdinov, and Andriy Mnih. (2008). “Bayesian probabilistic matrix factorization using Markov chain Monte Carlo.” In: Proceedings of the 25th International Conference on Machine learning (ICML 2008).
- QUOTE: Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets.