cmf | R Documentation |
Compute a low-rank matrix factorisation 3{ \min_{\mathbf A, \mathbf B} || (\mathbf X - \mathbf A \mathbf{B}^\top ) \circ \mathbf W ||_\mathrm F
min over A,B ||(X - A \%*\% t(B)) * W||_F
<span style="font-style:normal;">min</span><sub><b>A</b>,<b>B</b></sub>
||(<b>X</b> -
<b>A</b><b>B</b><sup><span style="font-style:normal;">T</span></sup>)
∘ <b>W</b>||<sub>F</sub>
subject to weights 3{\mathbf WW<b>W</b> (set to 0
where 3{\mathbf X
cmf(
X, nfac = 1,
const = list(list(const = "nonneg"), list(const = "nonneg")),
start = c("svd", "random"), ctol = 1e-04, maxit = 10
)
## S3 method for class 'cmf'
fitted(object, ...)
wcmls(X, A, W, ..., struc = NULL)
X |
The matrix for a low-rank approximation. |
nfac |
The rank of the factorisation; the number of columns in matrices 3{\mathbf A, \mathbf B |
A, B<b>A</b>, <b>B</b>.
The CMLS package function cmls
can solve
constrained multivariate least squares problems of the form:
An list of class cmf
containing the
3{\mathbf A, \mathbf B
A, B<b>A</b>, <b>B</b> matrices.
wcmls |
The 3{\mathbf B |
B<b>B</b> matrix solving the constrained weighted multivariate least squares problem.
deJuan2014
cmls
; the ALS package.
data(feems)
z <- feemscatter(feems$a, rep(25, 4), 'omit')
str(zf <- albatross:::cmf(unclass(z)))
str(albatross:::fitted.cmf(zf))
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