Feature extraction by correntropy based average neighborhood margin maximization

Average neighborhood margin maximization (ANMM) is a feature extraction method to make homogeneous points collect as near as possible and heterogeneous points disperse as far away as possible. To enhance the anti-noise ability of ANMM, correntropy based average neighborhood margin maximization (CANMM) is proposed in this paper. This method utilizes correntropy to substitute the Euclidean distance for measuring the similarity between the given data, and uses the maximum correntropy criterion to replace the maximum distance criterion, which makes CANMM more robust. The experimental results on three benchmark face databases validate the effectiveness of the proposed method.