방법 비교

	다중 양식 비음수 행렬 분해 주제 모델	음이 아닌 행렬 분해(NMF)
분야	딥러닝	머신러닝
계열	Machine learning	Latent structure
기원 연도	2010s	1999
창시자	Lee & Seung (NMF); multimodal extensions by various authors (~2010s)	Lee, D. D. & Seung, H. S.
유형	Multimodal topic model (NMF-based)	Matrix decomposition with non-negativity constraints
원전	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.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
별칭	Multimodal NMF, Multi-view NMF topic model, Joint NMF topic model, MM-NMF	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
관련	2	4
요약	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.	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.