Feature extraction and dimension reduction can be combined in one step using Principal Component Analysis ( PCA ) , linear discriminant analysis ( LDA ) , or canonical correlation analysis ( CCA ) techniques as a pre-processing step , followed by clustering by k -NN on feature vectors in reduced-dimension space .