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In this paper, a robust feature extraction method based on regularized correntropy criterion (RCC) is proposed for novelty detection. In RCC, the criterion aims to maximize the difference between the correntropy of the normal data with their mean and the correntropy of the novel data with the mean of normal data. Moreover, the optimal projection vectors in the proposed objective function can be obtained by the half-quadratic (HQ) optimization technique with an iterative manner. Experimental results on one synthetic data set and nine benchmark data sets for novelty detection demonstrate that the proposed method is superior to its related approaches.