방법 비교

	Multimodal LDA Topic Model	NMF 토픽 모델
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2003	1999
창시자	Blei, D. M. & Jordan, M. I.	Lee, D. D. & Seung, H. S.
유형	Probabilistic generative topic model (multimodal)	Matrix factorization / unsupervised topic model
원전	Blei, D. M. & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
별칭	Multimodal LDA, mm-LDA, multimodal topic model, cross-modal LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
관련	6	4
요약	Multimodal LDA extends Latent Dirichlet Allocation to jointly model multiple data modalities — most often text and images — within a single probabilistic topic framework. Each document or data instance is represented as a mixture of latent topics shared across modalities, enabling the model to discover coherent themes that align visual and linguistic content simultaneously.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.