estimateMode {fabia} | R Documentation |
estimateMode
: R implementation of estimateMode
.
estimateMode(X,maxiter=50,tol=0.001,alpha=0.1,a1=4.0,G1=FALSE)
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, |
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.
u |
the vector of estimated modes. |
xu |
X-u the mode centered data. |
Sepp Hochreiter
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
#--------------- # 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)