標(biāo)題: Titlebook: Elliptically Symmetric Distributions in Signal Processing and Machine Learning; Jean-Pierre Delmas,Mohammed Nabil El Korso,Frédéri Book 20 [打印本頁(yè)] 作者: Flange 時(shí)間: 2025-3-21 16:07
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning影響因子(影響力)
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning影響因子(影響力)學(xué)科排名
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning被引頻次
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning被引頻次學(xué)科排名
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning年度引用
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning年度引用學(xué)科排名
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning讀者反饋
書(shū)目名稱Elliptically Symmetric Distributions in Signal Processing and Machine Learning讀者反饋學(xué)科排名
作者: ABHOR 時(shí)間: 2025-3-21 22:14 作者: STING 時(shí)間: 2025-3-22 00:55
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 作者: BUDGE 時(shí)間: 2025-3-22 07:26 作者: 友好關(guān)系 時(shí)間: 2025-3-22 12:15
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作者: etidronate 時(shí)間: 2025-3-22 15:40 作者: etidronate 時(shí)間: 2025-3-22 19:25
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 作者: Multiple 時(shí)間: 2025-3-22 22:19
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作者: 高度表 時(shí)間: 2025-3-23 01:36
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 作者: attenuate 時(shí)間: 2025-3-23 08:33
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作者: 無(wú)能力 時(shí)間: 2025-3-23 11:27 作者: oxidant 時(shí)間: 2025-3-23 17:16 作者: 苦澀 時(shí)間: 2025-3-23 18:19 作者: preeclampsia 時(shí)間: 2025-3-24 01:13
Produktentwicklung und Konstruktionstechnikome practical uses of these geometric tools in the framework of elliptical distributions. This second part of the exposition is divided into three main axes: Riemannian optimization for covariance matrix estimation, Intrinsic Cramér–Rao bounds, and classification using Riemannian distances.作者: detach 時(shí)間: 2025-3-24 05:01 作者: harbinger 時(shí)間: 2025-3-24 08:06 作者: 和音 時(shí)間: 2025-3-24 14:31
The Fisher–Rao Geometry of CES Distributionsome practical uses of these geometric tools in the framework of elliptical distributions. This second part of the exposition is divided into three main axes: Riemannian optimization for covariance matrix estimation, Intrinsic Cramér–Rao bounds, and classification using Riemannian distances.作者: Expediency 時(shí)間: 2025-3-24 18:28
Linear Shrinkage of Sample Covariance Matrix or Matrices Under Elliptical Distributions: A Reviewelliptical sampling are shown to be functions of few key parameters only, such as elliptical kurtosis?and sphericity parameter. Similar results and estimators are derived for multiple population settings and applications of the studied shrinkage estimators are illustrated in portfolio optimization.作者: Gorilla 時(shí)間: 2025-3-24 19:22 作者: Acetaminophen 時(shí)間: 2025-3-25 01:47 作者: mighty 時(shí)間: 2025-3-25 05:07
https://doi.org/10.1007/978-3-658-22209-3xible model allows for potentially diverse and independent samples that may not follow identical distributions. By deriving a new decision rule, we demonstrate that maximum-likelihood parameter estimation?and classification?are simple, efficient, and robust compared to state-of-the-art methods.作者: 傻 時(shí)間: 2025-3-25 09:26
FEMDA: A Unified Framework for?Discriminant Analysisxible model allows for potentially diverse and independent samples that may not follow identical distributions. By deriving a new decision rule, we demonstrate that maximum-likelihood parameter estimation?and classification?are simple, efficient, and robust compared to state-of-the-art methods.作者: 褻瀆 時(shí)間: 2025-3-25 12:43 作者: 消息靈通 時(shí)間: 2025-3-25 18:05
Fritz Aulinger,Wilm Reerink,Wolfgang Riepe the proposed algorithms are designed to handle various patterns of missing values. At the end of the chapter, the performances of the proposed procedures are illustrated on simulated datasets with missing values. We share a link to a code repository for fully reproducible experiments.作者: 熒光 時(shí)間: 2025-3-25 23:10 作者: Licentious 時(shí)間: 2025-3-26 00:18
Methodisches Erfinden im Personalmanagementnce matrix?(SSCM). The asymptotic distributions?of these estimators are also derived. This enables us to unify the asymptotic distribution?of subspace projectors?adapted to the different models of the data and demonstrate various invariance properties that have impacts on the parameters to be estima作者: RACE 時(shí)間: 2025-3-26 05:01 作者: 精密 時(shí)間: 2025-3-26 12:02 作者: intention 時(shí)間: 2025-3-26 15:58 作者: 一罵死割除 時(shí)間: 2025-3-26 18:01
Elliptically Symmetric Distributions in Signal Processing and Machine Learning978-3-031-52116-4作者: APRON 時(shí)間: 2025-3-26 23:57
Background on Real and Complex Elliptically Symmetric Distributions,tions?are provided with their main properties. Finally, the estimation of the symmetry center?and scatter matrix?is briefly discussed through the sample mean?(SM), sample covariance matrix?(SCM) estimate, maximum estimate (ML), .-estimators, and Tyler’s .-estimators. Particular attention will be pai作者: 步兵 時(shí)間: 2025-3-27 02:39
Robust Estimation with Missing Values for Elliptical Distributions the proposed algorithms are designed to handle various patterns of missing values. At the end of the chapter, the performances of the proposed procedures are illustrated on simulated datasets with missing values. We share a link to a code repository for fully reproducible experiments.作者: 巧思 時(shí)間: 2025-3-27 06:48
Semiparametric Estimation in Elliptical Distributionsinfinite-dimensional nuisance term. In particular, the three building blocks of the semiparametric theory, that?are ., .?and ., will be introduced. By means of these abstract concepts, we define the semiparametric counterpart of the Fisher Information Matrix?(FIM) and the related semiparametric effi作者: 積習(xí)已深 時(shí)間: 2025-3-27 09:35
Performance Analysis of Subspace-Based Algorithms in CES Data Modelsnce matrix?(SSCM). The asymptotic distributions?of these estimators are also derived. This enables us to unify the asymptotic distribution?of subspace projectors?adapted to the different models of the data and demonstrate various invariance properties that have impacts on the parameters to be estima作者: 小隔間 時(shí)間: 2025-3-27 16:50 作者: SEVER 時(shí)間: 2025-3-27 20:50 作者: 外露 時(shí)間: 2025-3-27 23:26 作者: ARCHE 時(shí)間: 2025-3-28 05:46 作者: 搬運(yùn)工 時(shí)間: 2025-3-28 09:37 作者: MORPH 時(shí)間: 2025-3-28 11:33 作者: nostrum 時(shí)間: 2025-3-28 16:29
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作者: padding 時(shí)間: 2025-3-28 21:30
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 作者: Trypsin 時(shí)間: 2025-3-29 00:40
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作者: 結(jié)構(gòu) 時(shí)間: 2025-3-29 07:02
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作者: 含糊 時(shí)間: 2025-3-29 10:15 作者: Glutinous 時(shí)間: 2025-3-29 12:51 作者: paleolithic 時(shí)間: 2025-3-29 16:34
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作者: nitroglycerin 時(shí)間: 2025-3-29 20:57
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
