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In this paper, we propose an efficient nonlinear feature extraction method using kernel-based minimum squared error (KMSE). This improved method is referred to as reformative KMSE (RKMSE). In RKMSE, we use a linear combination of a small portion of samples that are selected from the training sample set, i.e. “significant nodes”, to approximate to the transform vector of KMSE in kernel space. As a result, RKMSE is much superior to naive KMSE in computational efficiency of feature extraction. Experimental results on several benchmark datasets illustrate that RKMSE can efficiently classify the data with high recognition correct rate.