do.nrsr: Non-convex Regularized Self-Representation

Description

In the standard, convex RSR problem (do.rsr), row-sparsity for self-representation is acquired using matrix \(\ell_{2,1}\) norm, i.e, \(\|W\|_{2,1} = \sum \|W_{i:}\|_2\). Its non-convex extension aims at achieving higher-level of sparsity using arbitrarily chosen \(\|W\|_{2,l}\) norm for \(l\in (0,1)\) and this exploits Iteratively Reweighted Least Squares (IRLS) algorithm for computation.

Usage

do.nrsr(X, ndim = 2, expl = 0.5, preprocess = c("null", "center",
  "scale", "cscale", "whiten", "decorrelate"), lbd = 1)

Arguments

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

expl

an exponent in \(\ell_{2,l}\) norm for sparsity. Must be in \((0,1)\), or \(l=1\) reduces to RSR problem.

preprocess

an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

lbd

nonnegative number to control the degree of self-representation by imposing row-sparsity.

Value

a named list containing

Y: an \((n\times ndim)\) matrix whose rows are embedded observations.
featidx: a length-\(ndim\) vector of indices with highest scores.
trfinfo: a list containing information for out-of-sample prediction.
projection: a \((p\times ndim)\) whose columns are basis for projection.

References

zhu_non-convex_2017Rdimtools

Examples

Run this code

# NOT RUN {
#### generate R12in72 dataset
X = aux.gensamples(n=100, dname="R12in72")

#### try different exponents for regularization
out1 = do.nrsr(X, expl=0.01)
out2 = do.nrsr(X, expl=0.1)
out3 = do.nrsr(X, expl=0.5)

#### visualize
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="NRSR::expl=0.01")
plot(out2$Y[,1], out2$Y[,2], main="NRSR::expl=0.1")
plot(out3$Y[,1], out3$Y[,2], main="NRSR::expl=0.5")
# }
# NOT RUN {
# }