手法を比較

	マルチモーダルNMFトピックモデル	潜在的ディリクレ配分法 (LDA)
分野	深層学習	機械学習
系統	Machine learning	Latent structure
提唱年	2010s	2003
提唱者	Lee & Seung (NMF); multimodal extensions by various authors (~2010s)	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
種類	Multimodal topic model (NMF-based)	Generative probabilistic topic model (three-level hierarchical Bayesian)
原典	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.
別名	Multimodal NMF, Multi-view NMF topic model, Joint NMF topic model, MM-NMF	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
関連	2	3
概要	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.