do.adr | R Documentation |
Adaptive Dimension Reduction \insertCiteding_adaptive_2002Rdimtools iteratively finds the best subspace to perform data clustering. It can be regarded as one of remedies for clustering in high dimensional space. Eigenvectors of a between-cluster scatter matrix are used as basis of projection.
do.adr(X, ndim = 2, ...)
X |
an (n\times p) matrix or data frame whose rows are observations. |
ndim |
an integer-valued target dimension. |
... |
extra parameters including
|
a named Rdimtools
S3 object containing
an (n\times ndim) matrix whose rows are embedded observations.
a (p\times ndim) whose columns are basis for projection.
a list containing information for out-of-sample prediction.
name of the algorithm.
do.ldakm
## 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]) ## compare ADR with other methods outADR = do.adr(X) outPCA = do.pca(X) outLDA = do.lda(X, label) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(outADR$Y, col=label, pch=19, main="ADR") plot(outPCA$Y, col=label, pch=19, main="PCA") plot(outLDA$Y, col=label, pch=19, main="LDA") par(opar)
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