do.ammc | R Documentation |
Adaptive Maximum Margin Criterion (AMMC) is a supervised linear dimension reduction method.
The method uses different weights to characterize the different contributions of the
training samples embedded in MMC framework. With the choice of a=0
, b=0
, and
lambda=1
, it is identical to standard MMC method.
do.ammc( X, label, ndim = 2, preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"), a = 1, b = 1, lambda = 1 )
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. |
preprocess |
an additional option for preprocessing the data.
Default is "center". See also |
a |
tuning parameter for between-class weight in [0,∞). |
b |
tuning parameter for within-class weight in [0,∞). |
lambda |
balance parameter for between-class and within-class scatter matrices in (0,∞). |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
lu_adaptive_2011Rdimtools
do.mmc
## load iris data data(iris) set.seed(100) subid = sample(1:150,50) X = as.matrix(iris[subid,1:4]) label = as.factor(iris[subid,5]) ## try different lambda values out1 = do.ammc(X, label, lambda=0.1) out2 = do.ammc(X, label, lambda=1) out3 = do.ammc(X, label, lambda=10) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, main="AMMC::lambda=0.1", pch=19, cex=0.5, col=label) plot(out2$Y, main="AMMC::lambda=1", pch=19, cex=0.5, col=label) plot(out3$Y, main="AMMC::lambda=10", pch=19, cex=0.5, col=label) par(opar)
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