Comparar métodos

	Asignación Latente de Dirichlet (LDA)	Factorización de Matrices No Negativas (NMF)
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Latent structure	Latent structure
Año de origen	2003	1999
Autor original	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	Lee, D. D. & Seung, H. S.
Tipo	Generative probabilistic topic model (three-level hierarchical Bayesian)	Matrix decomposition with non-negativity constraints
Fuente seminal	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Alias	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Relacionados	3	4
Resumen	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.	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.