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There are different types of non-negative matrix factorizations and one of these is related to [[probabilistic latent semantic analysis]] and the [[latent class model]].
The different types arise from using different [[cost function]]s (divergence functions) and/or by [[regularization (mathematics)|regularization]] of the '''W''' and/or '''H''' matrices<ref>[[Inderjit S. Dhillon]], [[Suvrit Sra]], "[http://books.nips.cc/papers/files/nips18/NIPS2005_0203.pdf Generalized Nonnegative Matrix Approximations with Bregman Divergences]", [[NIPS]], 2005.</ref>.
Although initially NMF is considered to differ from quantum vectorization (K-means clustering), it was later show
<ref>▼
Chris Ding, Xiaofeng He, and Horst D. Simon. "On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering". Proc. SIAM Int'l Conf. Data Mining (SDM'05), pp:606-610, April 2005.</ref>▼
NMF is equivalent to a relaxed form of K-means clustering.
== Uniqueness ==
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=== References ===
<references/>
▲<ref>
▲Chris Ding, Xiaofeng He, and Horst D. Simon. "On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering". Proc. SIAM Int'l Conf. Data Mining (SDM'05), pp:606-610, April 2005.</ref>
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