nmfdiv: Non-negative Matrix Factorization: Kullback-Leibler...
In fabia: FABIA: Factor Analysis for Bicluster Acquisition

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

nmfdiv: R implementation of nmfdiv.

1	nmfdiv(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 Kullback-Leibler divergence.

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, 1999, 2001.

The code is implemented in R.

object of the class Factorization. Containing LZ (estimated noise free data L Z), L (loading L), Z (factors Z), U (noise X-LZ), X (scaled data X).

Sepp Hochreiter

D. D. Lee and H. S. Seung, ‘Algorithms for non-negative matrix factorization’, In Advances in Neural Information Processing Systems 13, 556-562, 2001.

D. D. Lee and H. S. Seung, ‘Learning the parts of objects by non-negative matrix factorization’, Nature, 401(6755):788-791, 1999.

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 <- nmfdiv(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 <- nmfdiv(X,13)

extractPlot(resToy,ti="NMFDIV",Y=Y)


## End(Not run)