The correntropy is originally proposed to measure the similarity between two random variables and developed as a novel metrics for feature matching. As a kernel method, the parameter of kernel function is very important for correntropy metrics. In this paper, we propose an adaptive parameter selection strategy for correntropy metrics and deduce a close-form solution based on the Maximum Correntropy Criterion (MCC). Moreover, considering the correlation of localized features, we modify the classic correntropy into a block-wise metrics. We verify the proposed metrics in face recognition applications taking Local Binary Pattern (LBP) features. Combined with the proposed adaptive parameter selection strategy, the modified block-wise correntropy metrics could result in much better performance in the experiments. © 2013 Springer-Verlag.
CITATION STYLE
Tan, Y., Fang, Y., Li, Y., & Dai, W. (2013). Adaptive kernel size selection for correntropy based metric. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7728 LNCS, pp. 50–60). https://doi.org/10.1007/978-3-642-37410-4_5
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