Titlebook: Elliptically Symmetric Distributions in Signal Processing and Machine Learning; Jean-Pierre Delmas,Mohammed Nabil El Korso,Frédéri Book 20

nostrum

Produktentwicklung und Konstruktionstechnikon metric. The geometry induced on the parameters by this metric is then referred to as the Fisher–Rao information geometry. Interestingly, this yields a point of view that allows for leveraging many tools from differential geometry. After a brief introduction about these concepts, we will present s

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https://doi.org/10.1007/978-3-658-28085-7and multiple populations settings, respectively. In the single sample setting a popular linear shrinkage estimator is defined as a linear combination of the sample covariance matrix (SCM) with a scaled identity matrix. The optimal shrinkage coefficients minimizing the mean-squared error (MSE) under

Trypsin

Fritz Aulinger,Wilm Reerink,Wolfgang Riepeimation methods either assume a multivariate Gaussian distribution, or suppose an unstructured covariance matrix. However, in many applications, the signal is not well described by a Gaussian model, and very often the data can be efficiently approximated by a low-rank model, inducing a low-rank stru

结构

https://doi.org/10.1007/978-3-658-25863-4owing how it can be fruitfully applied to the joint estimation of the . and the . (or .) matrix of a set of elliptically distributed observations in the presence of an unknown density generator. A semiparametric model is a set of probablity density functions (pdfs) parameterized by a finite-dimensio

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Glutinous

paleolithic

https://doi.org/10.1007/978-3-658-12213-3lustering methods are highly useful in a variety of applications. For example, in the medical sciences, identifying clusters may allow for a comprehensive characterization of subgroups of individuals. However, in real-world data, the true cluster structure is often obscured by heavy-tailed noise, ar

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https://doi.org/10.1007/978-3-658-22209-3th non-Gaussian distributions or contaminated datasets. This is primarily due to their reliance on the Gaussian assumption, which lacks robustness. We first explain and review the classical methods to address this limitation and then present a novel approach that overcomes these issues. In this new