nnmf
Nonnegative matrix factorization
Description
[
factors the nbym matrix W
,H
] = nnmf(A
,k
)A
into nonnegative factors W
(nbyk
) and H
(k
bym). The factorization is not exact;
W*H
is a lowerrank 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 namevalue 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
Nonnegative RankTwo Approximation and Biplot
Load the sample data.
load fisheriris
Compute a nonnegative ranktwo approximation of the measurements of the four variables in Fisher's iris data.
rng(1) % For reproducibility
[W,H] = nnmf(meas,2);
H
H = 2×4
0.6945 0.2856 0.6220 0.2218
0.8020 0.5683 0.1834 0.0149
The first and third variables in meas
(sepal length and petal length, with coefficients 0.6945 and 0.6220, respectively) provide relatively strong weights to the first column of W
. The first and second variables in meas
(sepal length and sepal width, with coefficients 0.8020 and 0.5683, respectively) provide relatively strong weights to the second column of W
.
Create a biplot
of the data and the variables in meas
in the column space of W
.
biplot(H','Scores',W,'VarLabels',{'sl','sw','pl','pw'}); axis([0 1.1 0 1.1]) xlabel('Column 1') ylabel('Column 2')
Change Algorithm
Starting from a random array X
with rank 20, try a few iterations at several replicates using the multiplicative algorithm.
rng default % For reproducibility X = rand(100,20)*rand(20,50); opt = statset('MaxIter',5,'Display','final'); [W0,H0] = nnmf(X,5,'Replicates',10,... 'Options',opt,... 'Algorithm','mult');
rep iteration rms resid delta x 1 5 0.560887 0.0245182 2 5 0.66418 0.0364471 3 5 0.609125 0.0358355 4 5 0.608894 0.0415491 5 5 0.619291 0.0455135 6 5 0.621549 0.0299965 7 5 0.640549 0.0438758 8 5 0.673015 0.0366856 9 5 0.606835 0.0318931 10 5 0.633526 0.0319591 Final root mean square residual = 0.560887
Continue with more iterations from the best of these results using alternating least squares.
opt = statset('Maxiter',1000,'Display','final'); [W,H] = nnmf(X,5,'W0',W0,'H0',H0,... 'Options',opt,... 'Algorithm','als');
rep iteration rms resid delta x 1 24 0.257336 0.00271859 Final root mean square residual = 0.257336
Input Arguments
A
— Matrix to factorize
real matrix
Matrix to factorize, specified as a real matrix.
Example: rand(20,30)
Data Types: single
 double
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: [W,H] = nnmf(A,k,'Algorithm','mult','Replicates',10)
chooses the multiplicative update algorithm and ten replicates to improve the
result
Algorithm
— Factorization algorithm
'als'
(default)  'mult'
Factorization algorithm, specified as the commaseparated pair
consisting of 'Algorithm'
and
'als'
(alternating least squares) or
'mult'
(a multiplicative update
algorithm).
The 'als'
algorithm typically is more stable and
converges in fewer iterations. Each iteration takes longer. Therefore,
the default maximum is 50, which usually gives satisfactory results in
internal testing.
The 'mult'
algorithm typically has faster
iterations and requires more of them. The default maximum is 100. This
algorithm tends to be more sensitive to starting values and, therefore,
seems to benefit more from running multiple replications.
Example: 'Algorithm','mult'
Data Types: char
 string
Options
— Algorithm options
[]
(default)  structure returned by statset
Algorithm options, specified as the commaseparated pair consisting
of 'Options'
and a structure returned by the
statset
function.
nnmf
uses the following fields of the options
structure.
Field  Description  Values 

Display  Level of iterative display 

MaxIter  Maximum number of iterations  Positive integer. The default is
50 for the
'als' algorithm and
100 for the
'mult' algorithm. Unlike in
optimization settings, reaching
MaxIter iterations is treated as
convergence. 
TolFun  Termination tolerance on the change in size of the residual  Nonnegative value. The default is
1e4 . 
TolX  Termination tolerance on the relative change in the
elements of W and
H  Nonnegative value. The default is
1e4 . 
UseParallel  Indication to compute in parallel  Logical value. The default false
indicates not to compute in parallel, and
true indicates to compute in
parallel. Computing in parallel requires a Parallel Computing Toolbox™ license. 
UseSubstreams  Type of reproducibility when computing in parallel 
For details, see Reproducibility in Parallel Statistical Computations. 
Streams  A RandStream object or cell array of such
objects 

Example: 'Options',statset('Display','iter','MaxIter',50)
Data Types: struct
Replicates
— Number of times to repeat factorization
1
(default)  positive integer
Number of times to repeat the factorization, specified as the
commaseparated pair consisting of 'Replicates'
and a
positive integer. The algorithm chooses new random starting values for
W
and H
at each replication,
except at the first replication if you specify 'W0'
and 'H0'
. If you specify a value greater than
1
, you can obtain better results by setting
Algorithm
to 'mult'
. See
Change Algorithm.
Example: 10
Data Types: single
 double
Output Arguments
D
— Root mean square residual
nonnegative scalar
Root mean square residual, returned as a nonnegative scalar.
D = norm(A  W*H,'fro')/sqrt(n*m)
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
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, specify the Options
namevalue argument in the call to
this function and set the UseParallel
field of the
options structure to true
using
statset
:
Options=statset(UseParallel=true)
For more information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).
Version History
Introduced in R2008a
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