do.disr | R Documentation |
Diversity-Induced Self-Representation (DISR) is a feature selection method that aims at
ranking features by both representativeness and diversity. Self-representation controlled by
lbd1
lets the most representative features to be selected, while lbd2
penalizes
the degree of inter-feature similarity to enhance diversity from the chosen features.
do.disr( X, ndim = 2, preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"), lbd1 = 1, lbd2 = 1 )
X |
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. |
ndim |
an integer-valued target dimension. |
preprocess |
an additional option for preprocessing the data.
Default is "null". See also |
lbd1 |
nonnegative number to control the degree of regularization of the self-representation. |
lbd2 |
nonnegative number to control the degree of feature diversity. |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a length-ndim vector of indices with highest scores.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
liu_unsupervised_2017Rdimtools
do.rsr
## use 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 lbd combinations out1 = do.disr(X, lbd1=1, lbd2=1) out2 = do.disr(X, lbd1=1, lbd2=5) out3 = do.disr(X, lbd1=5, lbd2=1) out4 = do.disr(X, lbd1=5, lbd2=5) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(2,2)) plot(out1$Y, main="(lbd1,lbd2)=(1,1)", col=label, pch=19) plot(out2$Y, main="(lbd1,lbd2)=(1,5)", col=label, pch=19) plot(out3$Y, main="(lbd1,lbd2)=(5,1)", col=label, pch=19) plot(out4$Y, main="(lbd1,lbd2)=(5,5)", col=label, pch=19) par(opar)
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