手法を比較

	潜在的ディリクレ配分法 (LDA)	非負値行列因子分解 (NMF)	Word2Vec
分野	機械学習	機械学習	テキストマイニング
系統	Latent structure	Latent structure	Process / pipeline
提唱年	2003	1999	2013
提唱者	Blei, D. M.; Ng, A. Y.; Jordan, M. I.	Lee, D. D. & Seung, H. S.	Tomas Mikolov et al.
種類	Generative probabilistic topic model (three-level hierarchical Bayesian)	Matrix decomposition with non-negativity constraints	Neural word-embedding model
原典	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
別名	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
関連	3	4	4
概要	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.	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.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.