do.kqmi {Rdimtools} | R Documentation |
Kernel Quadratic Mutual Information
Description
Kernel Quadratic Mutual Information (KQMI) is a supervised linear dimension reduction method. Quadratic Mutual Information is an efficient nonparametric estimation method for Mutual Information for class labels not requiring class priors. The method re-states the estimation procedure in terms of kernel objective in the graph embedding framework.
Usage
do.kqmi(
X,
label,
ndim = 2,
preprocess = c("center", "scale", "cscale", "whiten", "decorrelate"),
t = 10
)
Arguments
X |
an |
label |
a length- |
ndim |
an integer-valued target dimension. |
preprocess |
an additional option for preprocessing the data.
Default is "center". See also |
t |
bandwidth parameter for heat kernel in |
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
Bouzas D, Arvanitopoulos N, Tefas A (2015). “Graph Embedded Nonparametric Mutual Information for Supervised Dimensionality Reduction.” IEEE Transactions on Neural Networks and Learning Systems, 26(5), 951–963.
See Also
Examples
## Not run:
## generate 3 different groups of data X and label vector
x1 = matrix(rnorm(4*10), nrow=10)-20
x2 = matrix(rnorm(4*10), nrow=10)
x3 = matrix(rnorm(4*10), nrow=10)+20
X = rbind(x1, x2, x3)
label = c(rep(1,10), rep(2,10), rep(3,10))
## try different kernel bandwidths
out1 = do.kqmi(X, label, t=0.01)
out2 = do.kqmi(X, label, t=1)
out3 = do.kqmi(X, label, t=100)
## visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=label, main="KQMI::t=0.01")
plot(out2$Y, col=label, main="KQMI::t=1")
plot(out3$Y, col=label, main="KQMI::t=100")
par(opar)
## End(Not run)