rdname softImpute-internal
simpute.als(
x,
J = 2,
thresh = 1e-05,
lambda = 0,
maxit = 100,
trace.it = TRUE,
warm.start = NULL,
final.svd = TRUE
)An m by n matrix with NAs. For large matrices can be of class
"Incomplete", in which case the missing values are represented as
pseudo zeros leading to dramatic storage reduction. x can have been
centered and scaled via biScale, and this information is carried
along with the solution.
Trevor to document this param
convergence threshold, measured as the relative change in the Frobenius norm between two successive estimates.
nuclear-norm regularization parameter. If lambda=0, the
algorithm reverts to "hardImpute", for which convergence is typically
slower, and to local minimum. Ideally lambda should be chosen so that
the solution reached has rank slightly less than rank.max. See also
lambda0() for computing the smallest lambda with a zero
solution.
maximum number of iterations.
with trace.it=TRUE, convergence progress is reported.
an svd object can be supplied as a warm start. This is
particularly useful when constructing a path of solutions with decreasing
values of lambda and increasing rank.max. The previous
solution can be provided directly as a warm start for the next.
only applicable to type="als". The alternating
ridge-regressions do not lead to exact zeros. With the default
final.svd=TRUE, at the final iteration, a one step unregularized
iteration is performed, followed by soft-thresholding of the singular
values, leading to hard zeros.