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In [16], the minimization problem is solved based on the covariance matrix estimation approach which requires a large number of snapshots.
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For mixing matrix estimation, our approach described in Section "Signal source detection for mixing matrix estimation (autosource selection)" relies on no weak-sparseness assumption and involves two parameters only, that is, the tolerance and the false-alarm probability.
The matrix approximation approach is based on the concept that most gene pairs in the large-scale genetic interaction screens have no significant interaction with each other, suggesting that the double-mutant fitness matrix W alone should carry enough information for the estimation of the vector w of single-mutant fitness effects under an appropriate null model (see Methods for details).
Due to the difficulties arising in state estimation in Model Predictive Control (MPC) algorithms, Kalman filtering and dynamic matrix control (DMC) estimation approaches were combined in the current work.
To illustrate the value of the novel activity estimation approach from matrices, it might be useful constructing a standard 2D-QSAR model and check is for predictivity of the synthesized top-20 design proposals in comparison to the matrix-derived conditional probability.
A novel two-dimensional (2-D) direction-of-arrival (DOA) estimation approach based on matrix reconstruction is proposed for coherent signals impinging on two parallel uniform linear arrays (ULAs).
Instead of using separate estimations for each matrix, we are interested in a joint estimation approach.
After having discussed the three approaches for the scatter matrix estimation in t-distributed data, we can introduce the classical radar detection problem.
The proposed SCA-based approach has two main stages: modal matrix estimation and modal displacement estimation.
The maximum likelihood approach is a good representative of the covariance matrix estimation techniques, which could yield better estimation performance [28 30].
To compare the models, the method described by DeLong et al. was used [ 34]: where areas under correlated ROC curves are compared through a non-parametric approach that applied generalized U-statistics on the covariance matrix estimation.
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