O-D matrix estimation
Covariance estimation on matrix manifolds
... The estimation of covariance matrices is a fundamental problem in multivariate anal- ysis and uncertainty ...covariance matrix is a consistent esti- mator, it performs poorly when the relative number of ...
150
Adaptive filtering for estimation of a low-rank positive semidefinite matrix
... x = kV T xk 2 (instead of y = V V T x). IV. C ONVERGENCE ISSUES The parameter estimation problem from the model (11) is a linear problem. When using either polar or square-root parameterizations to enforce the ...
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A Stein’s approach to covariance matrix estimation using regularization of Cholesky factor and log-Cholesky metric
... Covariance matrix (CM) estimation is at the core of most methods used to process multichannel data, in a wide variety of applications, including social science, life science, physics, engineering, ...The ...
10
Maximum likelihood covariance matrix estimation from two possibly mismatched data sets
... covariance matrix from two data sets, one whose covariance matrix R 1 is the sought one and another set of samples whose covariance matrix R 2 slightly differs from the sought one, due ...covariance ...
10
Robust covariance matrix estimation and portfolio allocation: the case of non-homogeneous assets
... Covariance Matrix (SCM) is an optimal estimator of the ...covariance estimation under non-Gaussian distributions [5, 6] intends to deal with this problem especially when N , the number of samples, is larger ...
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Contributions to probabilistic non-negative matrix factorization - Maximum marginal likelihood estimation and Markovian temporal models
... Non-negative matrix factorization (NMF) has become a popular dimensionality reduction technique, and has found applications in many different fields, such as audio signal process- ing, hyperspectral imaging, or ...
164
On the use of Empirical Likelihood for non-Gaussian clutter covariance matrix estimation
... improved estimation scheme when the clutter distribution is ...semi-parametric estimation method [11] which allows to estimate unknown parameters by using information contained in the observed data such as ...
6
Random Matrix Theory applied to the Estimation of Collision Multiplicities
... This estimation step is a key task in multi-packet reception approaches and in collision resolution ...random matrix theory and rely on eigenvalue ...covariance matrix of the observations are above a ...
12
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
... symmetric matrix estimation, our proof technique is readily extendable to more generic low-rank symmetric matrix or low-rank symmetric tensor ...
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Constrained Low-rank Matrix Estimation: Phase Transitions, Approximate Message Passing and Applications
... low-rank matrix estimation in the presence of constraints on the factors into which the matrix is ...Low-rank matrix factorization is one of the basic methods used in data analysis for ...
65
On the universality of noiseless linear estimation with respect to the measurement matrix
... Hadamard matrix of order ...features matrix Φ = f (W X) is obtained from the raw data matrix X by means of a random projection matrix W and a pointwise nonlinear activation f ...linear ...
14
Constraint Programming Formulation for the Elevator Trip Origin-Destination Matrix Estimation Problem
... ISRN I3S/RR-2014-01-FR Janvier 2014 - 16 pages Abstract: We present a constraint programming formulation for the elevator trip origin-destination matrix estimation problem, and propose different approaches ...
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Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
... calculus based on Theorem 1 for f (z) = z. IV. S IMULATIONS AND A PPLICATIONS In this section, we first corroborate our theoretical findings by comparing the classical plug-in estimator to our proposed estimator on ...
7
Invariance properties of the likelihood ratio for covariance matrix estimation in some complex elliptically contoured distributions
... random matrix decompositions: distributions, ...spherical matrix distributions, in: ...shrinkage estimation for elliptically symmetric distributions with unknown covariance matrix, ...
11
Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method
... of estimation prob- lems, where we show that there exists a gap between what currently known polynomial algorithms (in particular spectral methods and approximate message-passing) can do and what is expected ...
51
Covariance matrix estimation with heterogeneous samples
... Covariance matrix estimation in a Bayesian framework is an old ...covariance matrix in homogeneous ...covariance matrix of the training samples, say , were random with some appropriate joint ...
12
Estimation of the normalized coherency matrix through the SIRV model. Application to high resolution POLSAR data
... vised ML classification of normalized coherencies. How- ever, the span-NC description of POLSAR images raises new problems which still remain under investigation. The first issue concerns the use of span for testing the ...
9
Random Matrix Theory applied to the Estimation of Collision Multiplicities
... This estimation step is a key task in multi-packet reception approaches and in collision resolution ...random matrix theory and rely on eigenvalue ...covariance matrix of the observations are above a ...
11
Knowledge-aided Bayesian covariance matrix estimation in compound-Gaussian clutter
... covariance matrix R and τ k is a positive random variable, independent of g k ...covariance matrix estimation is generally ...covariance matrix has been advocated in ...covariance ...
5
Efficient Estimation of Scatter Matrix with Convex Structure under t-Distribution
... covariance matrix estimation under ...improves estimation accuracy. In the frame- work of robust estimation, the t-distribution is particularly suited to describe heavy-tailed ...efficient ...
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