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

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ABHOR

STING

Linear Shrinkage of Sample Covariance Matrix or Matrices Under Elliptical Distributions: A Reviewand 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

BUDGE

友好關(guān)系

Semiparametric Estimation in Elliptical Distributionsowing 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

etidronate

etidronate

Performance Analysis of Subspace-Based Algorithms in CES Data Modelsapplications in signal processing. The statistical performance of these subspace-based algorithms?depends on the deterministic and stochastic statistical model of the noisy linear mixture?of the data, the estimate of the projector associated with different estimates of the scatter/covariance of the

Multiple

Robust Bayesian Cluster Enumeration for RES Distributionslustering 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

高度表

FEMDA: A Unified Framework for?Discriminant Analysisth 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

attenuate

Learning Graphs from?Heavy-Tailed Dataultivariate Student’s .-distribution as a Laplacian matrix?associated to a graph whose node features (or signals) are observable. We design numerical algorithms, via the alternating direction method of multipliers, to learn connected, .-component, bipartite, and .-component bipartite graphs suitable