Exact(8)
The aforementioned error correction employed a single constraint.
We then change the error skewness using the aforementioned error distribution model.
More importantly, a significant performance improvement is expected in the joint estimation of the aforementioned error sources as opposed to their separate estimation.
If (hat{y}left( {{mathbf{x}}_{new} } right)) is bounded to (left[ {0, 1} right]) the smaller of the aforementioned error probabilities can simply be used.
The authors' intention in the performance comparison is not to include the existing estimators in which only one or two of the aforementioned error sources are considered.
Nevertheless, when the dominant species had to be predicted with the aforementioned error levels, the predictions that did not consider the dominant species were generally clearly more accurate than those refined with the predicted species.
The different estimates result from the proper choice of the aforementioned error function can be divided in two groups: (1) error functions based on the distance between the two matrices and (2) error functions based on the positive definite character of the difference ( R ^ y - γ Φ R c m Φ H ). Three different candidate methods were defined in [24] for the non-compressed case.
The covariance matrix of the aforementioned error, ξ = E { e e H } = R x x - R x y A H - A R y x + A R y y A H (22). Given that R x y = R y x = γ R x x and ∇ A H ξ = A R y y - A R x y, the optimal filter and the minimum error are given by, A o p = γ R x x R y y - 1 (23) ξ min = R x x ( I - γ R y y - 1 R x x ) (24).
Related(1)
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