# estimateMode: Estimation of the modes of the rows of a matrix In fabia: FABIA: Factor Analysis for Bicluster Acquisition

## Description

estimateMode: R implementation of estimateMode.

## Usage

 1 estimateMode(X,maxiter=50,tol=0.001,alpha=0.1,a1=4.0,G1=FALSE) 

## Arguments

 X matrix of which the modes of the rows are estimated. maxiter maximal number of iterations; default = 50. tol tolerance for stopping; default = 0.001. alpha learning rate; default = 0.1. a1 parameter of the width of the given distribution; default = 4. G1 kind of distribution, TRUE: G1, FALSE: G2; default = FALSE.

## Details

The mode is estimated by contrast functions G1

(1/a_1) * \ln (\cosh (a1*x))

or G2

- (1/a_1)*\exp(-1/2 * x*x)

The estimation is performed by gradient descent initialized by the median.

Implementation in R.

## Value

 u the vector of estimated modes. xu X-u the mode centered data.

Sepp Hochreiter

## References

A. Hyvaerinen, ‘Fast and Robust Fixed-Point Algorithms for Independent Component Analysis’, IEEE Transactions on Neural Networks 10(3):626-634, 1999.

fabia, fabias, fabiap, fabi, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, extractPlot, extractBic, plotBicluster, Factorization, projFuncPos, projFunc, estimateMode, makeFabiaData, makeFabiaDataBlocks, makeFabiaDataPos, makeFabiaDataBlocksPos, matrixImagePlot, fabiaDemo, fabiaVersion
  1 2 3 4 5 6 7 8 9 10 11 12 13 #--------------- # DEMO #--------------- dat <- makeFabiaDataBlocksPos(n = 100,l= 50,p = 10,f1 = 5,f2 = 5, of1 = 5,of2 = 10,sd_noise = 2.0,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]] modes <- estimateMode(X) modes\$u - apply(X, 1, median)