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Correntropy: Properties and Applications in Non-Gaussian Signal Processing
Abstract
The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are presented. As such correntropy has vastly different properties compared with second-order statistics that can be very useful in non-Gaussian signal processing, especially in the impulsive noise environment. Examples are presented to illustrate the technique.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
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Metadata
- Authors
- Liu, W., Pokharel, P. P., & Principe, J. C.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2007-11
- Volume
- 55
- Issue
- 11
- Pages
- 5286-5298
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2135160607
- Cited by
- 1,695
- References
- 2
- FWCI
- 21.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, W., Pokharel, P. P., & Principe, J. C. (2007). Correntropy: Properties and Applications in Non-Gaussian Signal Processing. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2007.896065
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