fabiasp {fabia}R Documentation

Factor Analysis for Bicluster Acquisition: Sparseness Projection (FABIASP)

Description

fabiasp: R implementation of fabias, therfore it is slow.

Usage


fabiasp(X,cyc,alpha,spz,p,norm=1)

Arguments

X the data matrix.
cyc number of cycles to run.
alpha sparseness loadings via projection (0.1 - 0.9).
spz sparseness factors (0.5 - 4.0).
p number of hidden factors = number of biclusters.
norm should the data be standardized, default = 1 (yes, using mean), 2 (yes, using median).

Details

Biclusters are found by sparse factor analysis where both the factors and the loadings are sparse.

Essentially the model is the sum of outer products of sparse vectors. The number of summands p is the number of biclusters.

X = L Z + U

X = sum_{i=1}^{p} L_i (Z_i )^T + U

If the nonzero components of the sparse vectors are grouped together then the outer product results in a matrix with a nonzero block and zeros elsewhere.

The model selection is performed by a variational approach according to Girolami 2001 and Palmer et al. 2006.

The prior has finit support, therefore after each update of the loadings they are projected to the finite support. The projection is done according to Hoyer, 2004: given an l_1-norm and an l_2-norm minimize the Euclidian distance to the original vector (currently the l_2-norm is fixed to 1). The projection is a convex quadratic problem which is solved iteratively where at each iteration at least one component is set to zero. Instead of the l_1-norm a sparseness measurement is used which relates the l_1-norm to the l_2-norm.

The code is implemented in R, therfore it is slow.

Value

LZ Estimated Noise Free Data: L Z
L Loadings: L
Z Factors: Z
Psi Noise variance: σ
lapla Variational parameter

Author(s)

Sepp Hochreiter

References

Mark Girolami, ‘A Variational Method for Learning Sparse and Overcomplete Representations’, Neural Computation 13(11): 2517-2532, 2001.

J. Palmer, D. Wipf, K. Kreutz-Delgado, B. Rao, ‘Variational EM algorithms for non-Gaussian latent variable models’, Advances in Neural Information Processing Systems 18, pp. 1059-1066, 2006.

Patrik O. Hoyer, ‘Non-negative Matrix Factorization with Sparseness Constraints’, Journal of Machine Learning Research 5:1457-1469, 2004.

See Also

fabi, fabia, fabiap, fabias, mfsc, nmfdiv, nmfeu, nmfsc, nprojfunc, projfunc, make_fabi_data, make_fabi_data_blocks, make_fabi_data_pos, make_fabi_data_blocks_pos, extract_plot, extract_bic, myImagePlot, PlotBicluster, Breast_A, DLBCL_B, Multi_A, fabiaDemo, fabiaVersion

Examples


#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(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]]


resEx <- fabiasp(X,50,0.8,1.0,3)

## Not run: 
#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(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]]

resToy <- fabiasp(X,200,0.6,1.0,13)

rToy <- extract_plot(X,resToy$L,resToy$Z,"ti=FABIASP",Y=Y)

#---------------
# DEMO2
#---------------

data(Breast_A)

X <- as.matrix(XBreast)

resBreast <- fabiasp(X,200,0.4,1.0,5)

rBreast <- extract_plot(X,resBreast$L,resBreast$Z,ti="FABIASP Breast cancer(Veer)")

#sorting of predefined labels
CBreast

#---------------
# DEMO3
#---------------

data(Multi_A)

X <- as.matrix(XMulti)

resMulti <- fabiasp(X,200,0.4,1.0,5)

rMulti <- extract_plot(X,resMulti$L,resMulti$Z,"ti=FABIASP Multiple tissues(Su)")

#sorting of predefined labels
CMulti

#---------------
# DEMO4
#---------------

data(DLBCL_B)

X <- as.matrix(XDLBCL)

resDLBCL <- fabiasp(X,200,0.6,1.0,5)

rDLBCL <- extract_plot(X,resDLBCL$L,resDLBCL$Z,ti="FABIASP Lymphoma(Rosenwald)")

#sorting of predefined labels
CDLBCL

## End(Not run)

[Package fabia version 0.1.1 Index]