projfunc {fabia} | R Documentation |
projfunc
: R implementation of projfunc
.
projfunc(s, k1, k2)
s |
data vector. |
k1 |
sparseness, l1 norm constraint. |
k2 |
l2 norm constraint. |
The projection is done according to Hoyer, 2004: given an l_1-norm and an l_2-norm minimize the Euclidean distance to the original vector. 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 |
sparse projected vector. |
Sepp Hochreiter
Patrik O. Hoyer, ‘Non-negative Matrix Factorization with Sparseness Constraints’, Journal of Machine Learning Research 5:1457-1469, 2004.
fabi
,
fabia
,
fabiap
,
fabias
,
fabiasp
,
mfsc
,
nmfdiv
,
nmfeu
,
nmfsc
,
nprojfunc
,
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
#--------------- # DEMO #--------------- size <- 30 sparseness <- 0.7 s <- as.vector(rnorm(size)) sp <- sqrt(1.0*size)-(sqrt(1.0*size)-1.0)*sparseness ss <- projfunc(s,k1=sp,k2=1) s ss