nmfeu {fabia} | R Documentation |
nmfeu
: R implementation of nmfeu
.
nmfeu(X,p=5,cyc=100)
X |
the data matrix. |
p |
number of hidden factors = number of biclusters; default = 5. |
cyc |
maximal number of iterations; default = 100. |
Non-negative Matrix Factorization represents positive matrix X by positive matrices L and Z.
Objective for reconstruction is Euclidean distance.
Essentially the model is the sum of outer products of vectors:
X = ∑_{i=1}^{p} λ_i z_i^T
where the number of summands p is the number of biclusters. The matrix factorization is
X = L Z
Here λ_i are from R^n, z_i from R^l, L from R^{n \times p}, Z from R^{p \times l}, and X from R^{n \times l}.
The model selection is performed according to D. D. Lee and H. S. Seung, 2001.
The code is implemented in R.
|
object of the class |
Sepp Hochreiter
Paatero, P and Tapper, U, ‘Least squares formulation of robust non-negative factor analysis’, Chemometr. Intell. Lab. 37: 23-35, 1997.
D. D. Lee and H. S. Seung, ‘Algorithms for non-negative matrix factorization’, In Advances in Neural Information Processing Systems 13, 556-562, 2001.
fabia
,
fabias
,
fabiap
,
fabi
,
fabiasp
,
mfsc
,
nmfdiv
,
nmfeu
,
nmfsc
,
extractPlot
,
extractBic
,
plotBicluster
,
Factorization
,
projFuncPos
,
projFunc
,
estimateMode
,
makeFabiaData
,
makeFabiaDataBlocks
,
makeFabiaDataPos
,
makeFabiaDataBlocksPos
,
matrixImagePlot
,
fabiaDemo
,
fabiaVersion
#--------------- # TEST #--------------- dat <- makeFabiaDataBlocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5, of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0, sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0) X <- dat[[1]] Y <- dat[[2]] X <- abs(X) resEx <- nmfeu(X,3) ## Not run: #--------------- # DEMO #--------------- dat <- makeFabiaDataBlocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5, of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0, sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0) X <- dat[[1]] Y <- dat[[2]] X <- abs(X) resToy <- nmfeu(X,13) extractPlot(resToy,ti="NMFEU",Y=Y) ## End(Not run)