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Classification is often performed after feature extraction. To improve the recognition performance, we could develop the optimal feature extraction method for a classification method. In this paper, we propose three feature extraction methods Discriminative Projection for Nearest Neighbor (DP-NN), Discriminative Projection for Nearest Mean (DP-NM) and Discriminative Projection for Nearest Feature Line (DP-NFL), which are optimal for classification methods Nearest Neighbor (NN), Nearest Mean (NM) and Nearest Feature Line (NFL), respectively. We also prove that DP-NN and DP-NM are equivalent to Linear Discriminant Analysis (LDA) under a certain condition. In the experiments, LDA, DP-NFL and SRC steered Discriminative Projection (SRC-DP) are used for feature extraction and then the extracted features are classified by NN, NM, NFL, Sparse Representation based Classification (SRC) and Collaborative …