Feature extraction and dimension reduction can be combined in one step using principal component analysis ( PCA ) , linear discriminant analysis ( LDA ) , canonical correlation analysis ( CCA ) , or non-negative matrix factorization ( NMF ) techniques as a pre-processing step followed by clustering by K-NN on feature vectors in reduced-dimension space .