projFuncPos {fabia} | R Documentation |
projFuncPos
: R implementation of projFuncPos
.
projFuncPos(s, k1, k2)
s |
data vector. |
k1 |
sparseness, l1 norm constraint. |
k2 |
l2 norm constraint. |
The projection minimize the Euclidean distance to the original vector given an l_1-norm and an l_2-norm and enforcing non-negativity.
The projection is a convex quadratic problem which is solved iteratively where at each iteration at least one component is set to zero.
In the applications, instead of the l_1-norm a sparseness measurement is used which relates the l_1-norm to the l_2-norm:
Implementation in R.
v |
non-negative sparse projected vector. |
Sepp Hochreiter
Patrik O. Hoyer, ‘Non-negative Matrix Factorization with Sparseness Constraints’, Journal of Machine Learning Research 5:1457-1469, 2004.
fabia
,
fabias
,
fabiap
,
fabi
,
fabiasp
,
mfsc
,
nmfdiv
,
nmfeu
,
nmfsc
,
extractPlot
,
extractBic
,
plotBicluster
,
Factorization
,
projFuncPos
,
projFunc
,
estimateMode
,
makeFabiaData
,
makeFabiaDataBlocks
,
makeFabiaDataPos
,
makeFabiaDataBlocksPos
,
matrixImagePlot
,
fabiaDemo
,
fabiaVersion
#--------------- # DEMO #--------------- size <- 30 sparseness <- 0.7 s <- as.vector(rnorm(size)) sp <- sqrt(1.0*size)-(sqrt(1.0*size)-1.0)*sparseness ss <- projFuncPos(s,k1=sp,k2=1) s ss