do.mmp {Rdimtools}

Maximum Margin Projection

Description

Maximum Margin Projection (MMP) is a supervised linear method that maximizes the margin between positive and negative examples at each local neighborhood based on same- and different-class neighborhoods depending on class labels.

Usage

do.mmp(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  numk = max(ceiling(nrow(X)/10), 2),
  alpha = 0.5,
  gamma = 50
)

Arguments

X

an (n\times p) matrix or data frame whose rows are observations.

label

a length-n vector of data class labels.

ndim

an integer-valued target dimension.

preprocess

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

numk

the number of neighboring points.

alpha

balancing parameter in [0,1].

gamma

weight for same-label data points with large magnitude.

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.

Author(s)

Kisung You

References

Xiaofei He, Deng Cai, Jiawei Han (2008). “Learning a Maximum Margin Subspace for Image Retrieval.” IEEE Transactions on Knowledge and Data Engineering, 20(2), 189–201.

Examples

## generate data of 3 types with clear difference
dt1  = aux.gensamples(n=20)-100
dt2  = aux.gensamples(n=20)
dt3  = aux.gensamples(n=20)+100

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2,dt3)
label  = rep(1:3, each=20)

## 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

## compare with PCA case for full-label case
## for missing label case from MMP computation
out1 = do.pca(X, ndim=2)
out2 = do.mmp(X, label_missing, numk=10)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, col=label, main="PCA projection")
plot(out2$Y, col=label, main="20% missing labels")
par(opar)