Linear Discriminant Analysis (LDA) originally aims to find a set of features that best separate groups of data. Since we need label information, LDA belongs to a class of supervised methods of performing classification. However, since it is based on finding suitable projections, it can still be used to do dimension reduction. We support both binary and multiple-class cases. Note that the target dimension ndim should be less than or equal to K-1, where K is the number of classes, or K=length(unique(label)). Our code automatically gives bounds on user's choice to correspond to what theory has shown. See the comments section for more details.

do.lda(X, label, ndim = 2)



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


a length-\(n\) vector of data class labels.


an integer-valued target dimension.


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.


name of the algorithm.

Limit of Target Dimension Selection

In unsupervised algorithms, selection of ndim is arbitrary as long as the target dimension is lower-dimensional than original data dimension, i.e., ndim < p. In LDA, it is not allowed. Suppose we have K classes, then its formulation on \(S_B\), between-group variance, has maximum rank of K-1. Therefore, the maximal subspace can only be spanned by at most K-1 orthogonal vectors.


Fisher RA (1936). “THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS.” Annals of Eugenics, 7(2), 179--188.

Fukunaga K (1990). Introduction to Statistical Pattern Recognition, Computer Science and Scientific Computing, 2nd ed edition. Academic Press, Boston. ISBN 978-0-12-269851-4.


Kisung You


# \donttest{
## use iris dataset
X     = as.matrix(iris[,1:4])
lab   = as.factor(iris[,5])

## compare with PCA
outLDA = do.lda(X, lab, ndim=2)
outPCA = do.pca(X, ndim=2)

## visualize
opar <- par(no.readonly=TRUE)
plot(outLDA$Y, col=lab, pch=19, main="LDA")
plot(outPCA$Y, col=lab, pch=19, main="PCA")

# }