nmfAlgorithm.SNMF_R(..., maxIter = 20000L, eta = -1, beta = 0.01, bi_conv = c(0, 10), eps_conv = 1e-04)
nmfAlgorithm.SNMF_L(..., maxIter = 20000L, eta = -1, beta = 0.01, bi_conv = c(0, 10), eps_conv = 1e-04)
W
and in H
in SNMF/R and
SNMF/L respectively. If eta < 0
, then it is set to the maximum value in
the target matrix is used.
H
and W
in SNMF/R and
SNMF/L respectively. Larger beta generates higher sparseness on H
(resp. W
). Too large beta is not recommended.
bi_conv=c(wminchange, iconv)
, with: wminchange
:iconv
:wminchange
) and column-clusters have not changed
for iconv
convergence checks.Convergence checks are performed every 5 iterations.
The algorithm SNMF/L solves a similar problem on
the transposed target matrix $A$, where $H$ and
$W$ swap roles, i.e. with sparsity constraints
applied to W
.