nnmf
Nonnegative matrix factorization
Description
[
factors the n-by-m matrix W
,H
] = nnmf(A
,k
)A
into nonnegative factors W
(n-by-k
) and H
(k
-by-m). The factorization is not exact;
W*H
is a lower-rank approximation to A
.
The factors W
and H
minimize the root mean
square residual D
between A
and
W*H
.
D = norm(A - W*H,'fro')/sqrt(n*m)
The factorization uses an iterative algorithm starting with random initial values
for W
and H
. Because the root mean square
residual D
might have local minima, repeated factorizations might
yield different W
and H
. Sometimes the
algorithm converges to a solution of lower rank than k, which can
indicate that the result is not optimal.
[
modifies the factorization using one or more name-value pair arguments. For example,
you can request repeated factorizations by setting W
,H
] = nnmf(A
,k
,Name,Value
)'Replicates'
to an integer value greater than 1.
Examples
Input Arguments
Output Arguments
References
[1] Berry, Michael W., Murray Browne, Amy N. Langville, V. Paul Pauca, and Robert J. Plemmons. “Algorithms and Applications for Approximate Nonnegative Matrix Factorization.” Computational Statistics & Data Analysis 52, no. 1 (September 2007): 155–73. https://doi.org/10.1016/j.csda.2006.11.006.
Extended Capabilities
Version History
Introduced in R2008a