The correntropy MACE filter

Jeong, Kyu-Hwa; Liu, Weifeng; Han, Seungju; Hasanbelliu, Erion; Prı́ncipe, José C.

doi:10.1016/j.patcog.2008.09.023

Cited by 62 publications

(24 citation statements)

References 20 publications

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“…In this study, the centered correntropy function V c (m) is employed in which the mean of the transformed data is subtracted so as to reduce the effect of output dc bias [23], [24]. Its definition is given by…”

Section: A Correntropy: Definition and Estimationmentioning

confidence: 99%

Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure

Garde

Sörnmo

Jané

et al. 2010

IEEE Trans. Biomed. Eng.

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Abstract-A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropybased spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

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Section: A Correntropy: Definition and Estimationmentioning

confidence: 99%

Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure

Garde

Sörnmo

Jané

et al. 2010

IEEE Trans. Biomed. Eng.

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“…Equation (4) was interpreted in [9,15,11] to mean that correntropy thus involves higher-order even moments of Z. Indeed [11, p. 874] "the kernel size σ K controls the emphasis of the higher order moments with respect to the second, since the higher order terms of the expansion decay faster for larger σ K .…”

Section: And K σ K (Z) As K(z) Using a Series Expansion For The Gausmentioning

confidence: 99%

“…Consider the terms in expansion (4) for a GGD with α = 3 -see These results are consistent with a convergent expansion in both cases. But we can see that the choice of σ K is quite crucial: choose it too large and correntropy will do no more than utilise second-order properties [11], but choose it too small and 'pathological' behaviour like that illustrated in Fig. 3(f) can arise.…”

Section: Examplesmentioning

confidence: 99%

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Correntropy: Implications of nonGaussianity for the moment expansion and deconvolution

2011

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The recently introduced correntropy function is an interesting and useful similarity measure between two random variables which has found myriad applications in signal processing. A series expansion for correntropy in terms of higher-order moments of the difference between the two random variables has been used to try to explain its statistical properties for uses such as deconvolution. We examine the existence and form of this expansion, showing that it may be divergent, e.g., when the difference has the Laplace distribution, and give sufficient conditions for its existence for differently characterized sub-Gaussian distributions. The contribution of the higher-order moments can be quite surprising, depending on the size of the Gaussian kernel in the definition of the correntropy. In the blind deconvolution setting we demonstrate that statistical exchangeability explains the existence of sub-optimal minima in the correntropy cost surface and show how the positions of these minima are controlled by the size of the Gaussian kernel.

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“…Correntropy is a generalization of correlation that extracts not only the second order information but also higher order moments of the joint distribution [15,16]. In the last few years, this concept has been successfully applied in several engineering applications such as time series modeling [17,18], nonlinearity test [19], matched filtering [20], object recognition [21] and independent component analysis [22]. Although, correntropy is similar to correlation by definition, recent studies have shown that it performs better than correlation while dealing with non-linear systems and non-Gaussian noise environments, without any significant increase in the computational cost.…”

Section: Introductionmentioning

confidence: 99%

A test of independence based on a generalized correlation function

Rao

Seth

et al. 2011

Signal Processing

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In this paper, we propose a novel test of independence based on the concept of correntropy. We explore correntropy from a statistical perspective and discuss its properties in the context of testing independence.We introduce the novel concept of parametric correntropy and design a test of independence based on it.We further discuss how the proposed test relaxes the assumption of Gaussianity. Finally, we discuss some computational issues related to the proposed method and compare it with state-of-the-art techniques.

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The correntropy MACE filter

Cited by 62 publications

References 20 publications

Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure

Correntropy-Based Spectral Characterization of Respiratory Patterns in Patients With Chronic Heart Failure

Correntropy: Implications of nonGaussianity for the moment expansion and deconvolution

A test of independence based on a generalized correlation function

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