Comparar métodos

	Factorización de Matrices No Negativas (NMF)	Asignación Latente de Dirichlet (LDA)
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Latent structure	Latent structure
Año de origen	1999	2003
Autor original	Lee, D. D. & Seung, H. S.	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
Tipo	Matrix decomposition with non-negativity constraints	Generative probabilistic topic model (three-level hierarchical Bayesian)
Fuente seminal	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
Relacionados	4	3
Resumen	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.	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.