Nonnegative Matrix Factorization // Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Sevinc V., Kontemeniotis N., Perdikis T. and Tsagris M. (2026). Non- negative matrix factorization using the R package nnmf doi:10.48550/arXiv.2607.20084, Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 doi:10.1109/TKDE.2012.51 and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 doi:10.1137/07069239X.