nmfeu: Non-negative Matrix Factorization: Euclidean Distance
In fabia: FABIA: Factor Analysis for Bicluster Acquisition

Description Usage Arguments Details Value Author(s) References See Also Examples

nmfeu: R implementation of nmfeu.

1	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 Factorization. Containing LZ (estimated noise free data L Z), L (loadings L), Z (factors Z), U (noise X-LZ), X (scaled data X).

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)