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

```
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

- X
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.

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

- b
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 J, Tan Y (2011).
“Adaptive Maximum Margin Criterion for Image Classification.”
In *2011 IEEE International Conference on Multimedia and Expo*, 1--6.

## Examples

```
## generate data of 3 types with clear difference
set.seed(100)
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 = rep(1:3, each=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$Y, pch=19, col=label, main="SAMMC::beta=0.1")
plot(out2$Y, pch=19, col=label, main="SAMMC::beta=1")
plot(out3$Y, pch=19, col=label, main="SAMMC::beta=10")
par(opar)
```