nprojfunc {fabia}R Documentation

Projection of a Vector to a Non-negative Sparse Vector

Description

nprojfunc: R implementation of nprojfunc.

Usage


nprojfunc(s, k1, k2)

Arguments

s data vector.
k1 sparseness, l1 norm constraint.
k2 l2 norm constraint.

Details

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.

Value

v non-negative sparse projected vector.

Author(s)

Sepp Hochreiter

References

Patrik O. Hoyer, ‘Non-negative Matrix Factorization with Sparseness Constraints’, Journal of Machine Learning Research 5:1457-1469, 2004.

See Also

fabi, fabia, fabiap, fabias, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, projfunc, 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

Examples


#---------------
# DEMO
#---------------

size <- 30
sparseness <- 0.7

s <- as.vector(rnorm(size))
sp <- sqrt(1.0*size)-(sqrt(1.0*size)-1.0)*sparseness

ss <- nprojfunc(s,k1=sp,k2=1)

s
ss


[Package fabia version 0.1.1 Index]