do.pfa | R Documentation |
Principal Feature Analysis \insertCitelu_2007_FeatureSelectionUsingRdimtools adopts an idea from the celebrated PCA for unsupervised feature selection.
do.pfa(X, ndim = 2, ...)
X |
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. |
ndim |
an integer-valued target dimension (default: 2). |
... |
extra parameters including
|
## 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]) ## compare with other methods out1 = do.pfa(iris.dat) out2 = do.lscore(iris.dat) out3 = do.fscore(iris.dat, iris.lab) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, pch=19, col=iris.lab, main="Principal Feature Analysis") plot(out2$Y, pch=19, col=iris.lab, main="Laplacian Score") plot(out3$Y, pch=19, col=iris.lab, main="Fisher Score") par(opar)
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