کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6939116 | 1449968 | 2018 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mixture correntropy for robust learning
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Correntropy is a local similarity measure defined in kernel space, hence can combat large outliers in robust signal processing and machine learning. So far, many robust learning algorithms have been developed under the maximum correntropy criterion (MCC), among which, a Gaussian kernel is generally used in correntropy. To further improve the learning performance, in this paper we propose the concept of mixture correntropy, which uses the mixture of two Gaussian functions as the kernel function. Some important properties of the mixture correntropy are presented. Applications of the maximum mixture correntropy criterion (MMCC) to extreme learning machine (ELM) and kernel adaptive filtering (KAF) for function approximation and data regression are also studied. Experimental results show that the learning algorithms under MMCC can perform very well and achieve better performance than the conventional MCC based algorithms as well as several other state-of-the-art algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 79, July 2018, Pages 318-327
Journal: Pattern Recognition - Volume 79, July 2018, Pages 318-327
نویسندگان
Badong Chen, Xin Wang, Na Lu, Shiyuan Wang, Jiuwen Cao, Jing Qin,