| 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