Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 48, Issue 7, Pages 2101-2113Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2727278
Keywords
Extreme learning machine (ELM); kernel mean p-power error (KMPE); principal component analysis (PCA); robust learning
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Funding
- 973 Program [2015CB351703]
- National NSF of China [91648208, 61372152]
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Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a non-second order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we apply the KMPE to extreme learning machine (ELM) and principal component analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and PCA-KMPE. Experimental results on synthetic and benchmark data show that the developed algorithms can achieve better performance when compared with some existing methods.
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