mmle_dirber_mcem: NMF with MMLE
In alumbreras/NBMF: Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization
View source: R/MCEM - DirBer.R
Usage
1
2
mmle_dirber_mcem(V, initW, alpha = 1, maxiters = 100, nsamples = 50,
burnin = 0.5)
Arguments
V
matrix to be factorized
maxiters
maximum number of iterations
nsamples
number of samples per iteration in the E-step
K
number of latent factors (or dimensions)
W
initial matrix W
mc
mcem or saem.
Details
The number of iterations is set to maxiters.
In the future, there should be a convergence check in case
the KL converges before maxiters.
Value
W last estimator of W
W_traces a K x samples - matrix vector with the evolution of row norms
alumbreras/NBMF documentation built on July 28, 2020, 9:58 a.m.
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View source: R/MCEM - DirBer.R
Usage
1 2 | mmle_dirber_mcem(V, initW, alpha = 1, maxiters = 100, nsamples = 50,
burnin = 0.5)
|
Arguments
V |
matrix to be factorized |
maxiters |
maximum number of iterations |
nsamples |
number of samples per iteration in the E-step |
K |
number of latent factors (or dimensions) |
W |
initial matrix W |
mc |
mcem or saem. |
Details
The number of iterations is set to maxiters. In the future, there should be a convergence check in case the KL converges before maxiters.
Value
W last estimator of W W_traces a K x samples - matrix vector with the evolution of row norms
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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