Titlebook: Matrix Information Geometry; Frank Nielsen,Rajendra Bhatia Book 2013 Springer-Verlag Berlin Heidelberg 2013 Audio Signal Processing.Covari

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Information Geometry of Covariance Matrix: Cartan-Siegel Homogeneous Bounded Domains, Mostow/Berger to non-singular parameterization transformations. For Doppler/Array/STAP Radar Processing, Information Geometry Approach will give key role to Homogenous Symmetric bounded domains geometry. For Radar, we will observe that Information Geometry metric could be related to Kähler metric, given by Hessi

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On the Use of Matrix Information Geometry for Polarimetric SAR Image Classificationic properties can be a real advantage. However, most classification techniques are developed under a Gaussian assumption of the signal and compute cluster centers using the standard arithmetical mean. This paper will present classification results on simulated and real images using a non-Gaussian si

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Doppler Information Geometry for Wake Turbulence Monitoringand applied for wake turbulence monitoring. With the methods of information geometry, we consider all the Toeplitz Hermitian Positive Definite covariance matrices of order . as a manifold .. Geometries of covariance matrices based on two kinds of radar data models are presented. Finally, a radar det

Inflamed

Review of the Application of Matrix Information Theory in Video Surveillanced specific person identification, overcrowding statistics and congestion control, strange situation detection and alarming, interactive surveillance using multiple cameras and so on. Video surveillance mainly involves modeling of background, detection of motion, classification of moving objects and

AMOR

Comparative Evaluation of Symmetric SVD Algorithms for Real-Time Face and Eye Trackingffective tool in computer vision in a number of aspects, such as: face recognition, eye tracking etc. At the present state of the art fast and fixed-point power efficient SVD algorithm needs to be developed for real-time embedded computing. The work in this paper is the genesis of an attempt to buil

Anticoagulants

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Mining Matrix Data with Bregman Matrix Divergences for Portfolio Selection model and assumed that investors only care for mean and variance. The mean-variance portfolio theory was born. As it name suggests, mean-variance theory is predicated on simple assumptions that are unfortunately seldomly met in real life. Indeed, it is now a well-established fact that for a host of

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Learning Mixtures by Simplifying Kernel Density Estimatorsoften using Expectation–Maximization or Kernel Density Estimation. In addition to these well known algorithms, new and promising stochastic modeling methods include Dirichlet Process mixtures and .-Maximum Likelihood Estimators. Most of the methods, including Expectation–Maximization, lead to compac