Salīdzināt metodes

	Multimodal NMF Topic Model	Latent Dirichlet Allocation (LDA)	Neatūru matricas faktorizācija (NMF)
Nozare	Dziļā mācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Latent structure	Latent structure
Izcelsmes gads	2010s	2003	1999
Autors	Lee & Seung (NMF); multimodal extensions by various authors (~2010s)	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	Lee, D. D. & Seung, H. S.
Tips	Multimodal topic model (NMF-based)	Generative probabilistic topic model (three-level hierarchical Bayesian)	Matrix decomposition with non-negativity constraints
Pirmavots	Cai, D., He, X., Han, J., & Huang, T. S. (2011). Graph regularized NMF. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1548–1560.	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.
Citi nosaukumi	Multimodal NMF, Multi-view NMF topic model, Joint NMF topic model, MM-NMF	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Saistītās	2	3	4
Kopsavilkums	Multimodal NMF Topic Model extends Non-negative Matrix Factorization to simultaneously discover latent topics across multiple data modalities — such as text and images — by enforcing shared or aligned low-rank factor matrices. It uncovers coherent, interpretable topics that jointly explain patterns in both textual and visual (or other) feature spaces.	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.