do.specu {Rdimtools}

Unsupervised Spectral Feature Selection

Description

SPEC algorithm selects features from the data via spectral graph approach. Three types of ranking methods that appeared in the paper are available where the graph laplacian is built via RBF kernel.

Usage

do.specu(
  X,
  ndim = 2,
  sigma = 1,
  ranking = c("method1", "method2", "method3"),
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate")
)

Arguments

X

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

ndim

an integer-valued target dimension.

sigma

bandwidth parameter for RBK kernel of type S_{i,j} = \exp(-\|x_i - x_j \|^2 / 2\sigma^2 ).

ranking

types of feature scoring method. See the paper in the reference for more details.

preprocess

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

Value

a named list containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

sscore

a length-p vector of spectral feature scores.

featidx

a length-ndim vector of indices with highest scores.

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

Zhao Z, Liu H (2007). “Spectral Feature Selection for Supervised and Unsupervised Learning.” In Proceedings of the 24th International Conference on Machine Learning - ICML '07, 1151–1157.

See Also

do.specs

Examples

## use iris data
## it is known that feature 3 and 4 are more important.
data(iris)
set.seed(100)
subid    = sample(1:150,50)
iris.dat = as.matrix(iris[subid,1:4])
iris.lab = as.factor(iris[subid,5])


## try different ranking methods
mysig = 6
out1  = do.specu(iris.dat, sigma=mysig, ranking="method1")
out2  = do.specu(iris.dat, sigma=mysig, ranking="method2")
out3  = do.specu(iris.dat, sigma=mysig, ranking="method3")

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=iris.lab, main="SPECU::method1")
plot(out2$Y, pch=19, col=iris.lab, main="SPECU::method2")
plot(out3$Y, pch=19, col=iris.lab, main="SPECU::method3")
par(opar)