Vertaile menetelmiä

	Selitettävä LDA-aihemalli	Ei-negatiivinen matriisihajotelma (NMF)
Tieteenala	Syväoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Latent structure
Syntyvuosi	2003 (LDA); 2018–present (explainability extensions)	1999
Kehittäjä	Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors	Lee, D. D. & Seung, H. S.
Tyyppi	Probabilistic generative topic model with interpretability enhancements	Matrix decomposition with non-negativity constraints
Alkuperäislähde	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.
Rinnakkaisnimet	Explainable LDA, Interpretable LDA, XAI-LDA, Transparent Topic Model	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Liittyvät	4	4
Tiivistelmä	Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery.	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.