مقایسهٔ روش‌ها

	تجزیه ماتریس نامنفی (NMF)	Word2Vec
حوزه	یادگیری ماشین	متن‌کاوی
خانواده	Latent structure	Process / pipeline
سال پیدایش	1999	2013
پدیدآور	Lee, D. D. & Seung, H. S.	Tomas Mikolov et al.
نوع	Matrix decomposition with non-negativity constraints	Neural word-embedding model
منبع بنیادین	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.
نام‌های دیگر	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
مرتبط	4	4
خلاصه	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.