An Analysis of Unsupervised Signal Processing Methods in the Context of Correlated Sources

Neves, Aline; Wada, Cristina; Suyama, Ricardo; Attux, Romis; Romano, João Marcos Travassos

doi:10.1007/978-3-642-00599-2_11

Cited by 8 publications

(7 citation statements)

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“…In [6], correntropy is used in a supervised scenario with impulsive noise, outperforming LMS in system identification and noise cancellation. In [1], [7], correntropy is used to perform blind equalization (which is discussed in detail in Section III-C), outperforming classical methods like the CMA [8] in the case of correlated sources. Nonetheless, CMA may have a better performance if the sources are iid [7], [9].…”

Section: V(x Y ) = E[g(x|y σmentioning

confidence: 99%

“…In [1], [7], correntropy is used to perform blind equalization (which is discussed in detail in Section III-C), outperforming classical methods like the CMA [8] in the case of correlated sources. Nonetheless, CMA may have a better performance if the sources are iid [7], [9]. In [10], correntropy is used as a unifying instantaneous blind source separation criterion, capable of separating iid sources, which requires higher-order statistics (HOS), and also of separating temporally-correlated Gaussian sources with distinct spectra, which demands temporal information.…”

Section: V(x Y ) = E[g(x|y σmentioning

confidence: 99%

“…Since correntropy takes into account the statistical and temporal structures of the signals, it has shown a good performance when treating correlated sources, a situation that classical methods fail to equalize [7]. Another advantage of the correntropy-based method is its reduced computational complexity in comparison with the PDF matching-based criteria -although it can demand an elevated number of samples for estimation.…”

Section: ) Supervised Equalizationmentioning

confidence: 99%

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An Introduction to Information Theoretic Learning, Part II: Applications

Silva

Fantinato

Canuto

et al. 2016

JCIS

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Abstract-This is the second part of the introductory tutorial about information theoretic learning, which, after the theoretical foundations presented in Part I, now discusses the concepts of correntropy, a new similarity measure derived from the quadratic entropy, and presents example problems where the ITL framework can be successfully applied: dynamic modelling, equalization, independent component analysis and cluster analysis.

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Section: V(x Y ) = E[g(x|y σmentioning

confidence: 99%

Section: V(x Y ) = E[g(x|y σmentioning

confidence: 99%

Section: ) Supervised Equalizationmentioning

confidence: 99%

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An Introduction to Information Theoretic Learning, Part II: Applications

Silva

Fantinato

Canuto

et al. 2016

JCIS

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“…The use of correntropy in blind deconvolution was a significant step in the direction of a better understanding of the problem under non-independent signals, but there remain plenty of unanswered questions regarding the behavior of classical approaches within this formulation. Some of these questions were addressed in [6], which presents elements of comparison between the CM algorithm (CMA) and the correntropy-based method. In this work, however, a further step is taken with the aid of two novel criteria that, aside from their intrinsic relevance, serve as bridges to relate the CMA to the ITL solution.…”

Section: Introductionmentioning

confidence: 99%

Blind Deconvolution of Correlated Sources Based on Second-Order Statistics

Fantinato¹,

Attux²,

Neves³

et al. 2013

Anais De XXXI Simpósio Brasileiro De Telecomunicações

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The blind deconvolution of signals composed of statistically dependent samples is an important practical problem whose understanding still requires the clarification of many theoretical points. In this work, we present an analysis of this problem that includes two well-established methods -the canonical constant modulus algorithm (CMA) and a correntropybased method -and two novel strategies that explore the temporal profile of the signal of interest. These techniques are compared in a number of representative scenarios, where it will be possible to form a clearer view of their potentialities and also of some peculiarities of the problem itself.

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“…A correntropy-based minimum average correlation energy (MACE) filter is given in [11]. Further discussions on correntropy can be found in [17] and [20].…”

Section: Introductionmentioning

confidence: 99%

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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An Analysis of Unsupervised Signal Processing Methods in the Context of Correlated Sources

Cited by 8 publications

References 8 publications

An Introduction to Information Theoretic Learning, Part II: Applications

An Introduction to Information Theoretic Learning, Part II: Applications

Blind Deconvolution of Correlated Sources Based on Second-Order Statistics

Correntropy: Implications of nonGaussianity for the moment expansion and deconvolution

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