A fast procedure for nonnegative matrix factorization.
1 2 3 4 5 6 7  fast_nmf(dat, k, type = "KL", tol = 1e08)
fast_nmf_KL(dat, k, tol = 1e08)
fast_nmf_Fr(dat, k, tol = 1e08)
fast_nmf_Al(dat, k, tol = 1e08)

dat 
Input data: can be a table or a data frame (but the data frame must have only two columns). 
k 
Numeric specification of the number of latent axes to compute. 
type 
Character specification of the type of optimization: can in the current implementation be either 
tol 
Numeric specification of the convergence criterion. 
A list with components:

The coordinates of the first set of levels (viz. the row levels of a frequency table). 

The coordinates of the second set of levels (viz. the column levels of a frequency table). 
Lee, D. D. and H. S. Seung (1999) Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791.
Lee, D. D. and H. S. Seung (2001) Algorithms for nonnegative matrix factorization. Advances in neural information processing systems 13, 556–562.
1 2 3 4  SndT_Fra < read.table(system.file("extdata", "SndT_Fra.txt", package = "svs"),
header = TRUE, sep = "\t", quote = "\"", encoding = "UTF8")
nmf.SndT_Fra < fast_nmf(SndT_Fra, k = 7)
nmf.SndT_Fra

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