So sánh phương pháp

	Mô hình Chủ đề NMF Đa phương thức	Phân tích ma trận không âm (NMF)
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Latent structure
Năm ra đời	2010s	1999
Người khởi xướng	Lee & Seung (NMF); multimodal extensions by various authors (~2010s)	Lee, D. D. & Seung, H. S.
Loại	Multimodal topic model (NMF-based)	Matrix decomposition with non-negativity constraints
Công trình gốc	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.
Tên gọi khác	Multimodal NMF, Multi-view NMF topic model, Joint NMF topic model, MM-NMF	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation
Liên quan	2	4
Tóm tắt	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.