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On the Geometry of Covariance Matrices
IEEE Signal Processing Letters, 2013We introduce and compare certain distance measures between covariance matrices. These originate in information theory, quantum mechanics and optimal transport. More specifically, we show that the Bures/Hellinger distance between covariance matrices coincides with the Wasserstein-2 distance between the corresponding Gaussian distributions.
Lipeng Ning +2 more
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On the estimation of structured covariance matrices
Automatica, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
ZORZI, MATTIA, FERRANTE, AUGUSTO
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Estimation of hyperspectral covariance matrices
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011Estimation of covariance matrices is a fundamental step in hyperspectral remote sensing where most detection algorithms make use of the covariance matrix in whitening procedures. We present a simple method to improve the estimation of the eigenvalues of a sample covariance matrix. With the improved eigenvalues we construct an improved covariance matrix.
Avishai Ben-David, Charles E. Davidson
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Dimension from covariance matrices
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems.
T. L. Carroll, J. M. Byers
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On testing for homogeneity of the covariance matrices
Korean Journal of Computational & Applied Mathematics, 2001Summary: Testing equality of covariance matrices of \(k\) populations has long been an interesting issue in statistical inference. To overcome the sparseness of data points in a high-dimensional space and deal with the general cases, we suggest several projection pursuit type statistics.
Zhang, Xiaoning +2 more
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Visualizing distributions of covariance matrices
Journal of Data Science, Statistics, and VisualisationStatistical graphics are generally designed for visualizing data, but in this case our primary goal is to understand complex multivariate models that might be used as prior distributions for models with unknown covariance matrices. Visualizing a distribution of covariance matrices is a step beyond visualizing a single covariance matrix or a single ...
Tomoki Tokuda +4 more
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Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
A standard problem in many classification tasks is how to model feature vectors whose elements are highly correlated. If multi-variate Gaussian distributions are used to model the data then they must have full covariance matrices to accurately do so. This requires a large number of parameters per distribution which restricts the number of distributions
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A standard problem in many classification tasks is how to model feature vectors whose elements are highly correlated. If multi-variate Gaussian distributions are used to model the data then they must have full covariance matrices to accurately do so. This requires a large number of parameters per distribution which restricts the number of distributions
openaire +2 more sources