Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are both linear transformation techniques. Linear Discriminant Analysis is supervised learning (available classifier), whilst principle component analysis is unsupervised learning (no available classifier).
As an example for both techniques, R’s build in data set “iris” is used.
For further information, please refer to this text 1.
1.
Coghlan A. A Little Book of R For Multivariate Analysis [Internet]. 0.1. Cambridge; 2014. 47 p. Available from: https://media.readthedocs.org/pdf/little-book-of-r-formultivariate- analysis/latest/little-book-of-r-for-multivariate-analysis.pdf