Results 11 to 20 of about 285,691 (282)
Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations
IEEE Access, 2020 Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise ...
Yanmin Zhu +3 more
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A finite-difference method for linearization in nonlinear estimation algorithms [PDF]
Modeling, Identification and Control, 1998 Linearizations of nonlinear functions that are based on Jacobian matrices often cannot be applied in practical applications of nonlinear estimation techniques. An alternative linearization method is presented in this paper.
Tor S. Schei
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Shrinkage Estimators for Covariance Matrices [PDF]
Biometrics, 2001 Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big.
Daniels, Michael J., Kass, Robert E.
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On the Properties of Estimates of Monotonic Mean Vectors for Multivariate Normal Distributions [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 .Problems concerning estimation of parameters and determination the statistic, when it is known a priori that some of these parameters are subject to certain order restrictions, are of considerable interest.
Abouzar Bazyari
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Remote Sensing, 2022 Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds.
Ronghua Yang +3 more
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IEEE Access, 2022 Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks.
Imtiaz Nasim, Ahmed S. Ibrahim
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Regularized Transport Between Singular Covariance Matrices [PDF]
IEEE Transactions on Automatic Control, 2021 We consider the problem of steering a linear stochastic system between two end-point degenerate Gaussian distributions in finite time. This accounts for those situations in which some but not all of the state entries are uncertain at the initial, t = 0, and final time, t = T .
Valentina Ciccone +3 more
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Linear Pooling of Sample Covariance Matrices [PDF]
IEEE Transactions on Signal Processing, 2022 We consider the problem of estimating high-dimensional covariance matrices of $K$-populations or classes in the setting where the sample sizes are comparable to the data dimension. We propose estimating each class covariance matrix as a distinct linear combination of all class sample covariance matrices.
Tyler, David E +3 more
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Local Laws for Sparse Sample Covariance Matrices
Mathematics, 2022 We proved the local Marchenko–Pastur law for sparse sample covariance matrices that corresponded to rectangular observation matrices of order n×m with n/m→y (where y>0) and sparse probability npn>logβn (where β>0).
Alexander N. Tikhomirov +1 more
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Estimating the power spectrum covariance matrix with fewer mock samples [PDF]
, 2015 The covariance matrices of power-spectrum (P(k)) measurements from galaxy surveys are difficult to compute theoretically. The current best practice is to estimate covariance matrices by computing a sample covariance of a large number of mock catalogues ...
Pearson, David W., Samushia, Lado
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