So sánh phương pháp

	Mô hình chủ đề LDA đa phương thức	Mô hình Chủ đề NMF
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2003	1999
Người khởi xướng	Blei, D. M. & Jordan, M. I.	Lee, D. D. & Seung, H. S.
Loại	Probabilistic generative topic model (multimodal)	Matrix factorization / unsupervised topic model
Công trình gốc	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.
Tên gọi khác	Multimodal LDA, mm-LDA, multimodal topic model, cross-modal LDA	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Liên quan	6	4
Tóm tắt	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.