Regularized correntropy criterion based feature extraction for novelty detection
In this paper, a novel feature extraction method based on regularized correntropy criterion (FEND-RCC) is proposed for novelty detection. In FEND-RCC, the presented 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 the normal data. Moreover, the optimal projection vectors in the objective function of FEND-RCC are iteratively obtained by the half-quadratic optimization technique. Experimental results on two synthetic data sets and thirteen benchmark data sets for novelty detection demonstrate that FEND-RCC is superior to its related approaches.
correntropy feature extraction novelty detection half-quadratic optimization