do.sammc: Semi-Supervised Adaptive Maximum Margin Criterion

Description

Semi-Supervised Adaptive Maximum Margin Criterion (SAMMC) is a semi-supervised variant of AMMC by making use of both labeled and unlabeled data.

Usage

do.sammc(
  X,
  label,
  ndim = 2,
  type = c("proportion", 0.1),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  a = 1,
  b = 1,
  lambda = 1,
  beta = 1
)

Arguments

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

label

a length-\(n\) vector of data class labels.

ndim

an integer-valued target dimension.

type

a vector of neighborhood graph construction. Following types are supported; c("knn",k), c("enn",radius), and c("proportion",ratio). Default is c("proportion",0.1), connecting about 1/10 of nearest data points among all data points. See also aux.graphnbd for more details.

preprocess

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

tuning parameter for between-class weight in \([0,\infty)\).

tuning parameter for within-class weight in \([0,\infty)\).

lambda

balance parameter for between-class and within-class scatter matrices in \((0,\infty)\).

beta

balance parameter for within-class scatter of the labeled data and consistency of the whole data in \((0,\infty)\).

Value

a named list containing

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

References

lu_adaptive_2011Rdimtools

Examples

Run this code

# NOT RUN {
## generate data of 3 types with clear difference
dt1  = aux.gensamples(n=33)-50
dt2  = aux.gensamples(n=33)
dt3  = aux.gensamples(n=33)+50

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2,dt3)
label  = c(rep(1,33), rep(2,33), rep(3,33))

## copy a label and let 20% of elements be missing
nlabel = length(label)
nmissing = round(nlabel*0.20)
label_missing = label
label_missing[sample(1:nlabel, nmissing)]=NA

## try different balancing
out1 = do.sammc(X, label_missing, beta=0.1)
out2 = do.sammc(X, label_missing, beta=1)
out3 = do.sammc(X, label_missing, beta=10)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="SAMMC::beta=0.1")
plot(out2$Y, main="SAMMC::beta=1")
plot(out3$Y, main="SAMMC::beta=10")
par(opar)
# }
# NOT RUN {
# }