Rdimtools (version 1.1.2)

do.lda: Linear Discriminant Analysis

Description

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.

Usage

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

Value

a named Rdimtools S3 object containing

Y

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

projection

a \((p\times ndim)\) whose columns are basis for projection.

algorithm

name of the algorithm.

Arguments

X

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

label

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

ndim

an integer-valued target dimension.

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.

Author

Kisung You

References

fisher_use_1936Rdimtools

fukunaga_introduction_1990Rdimtools

Examples

# \donttest{
## use iris dataset
data(iris)
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)
par(mfrow=c(1,2))
plot(outLDA$Y, col=lab, pch=19, main="LDA")
plot(outPCA$Y, col=lab, pch=19, main="PCA")
par(opar)
# }