The problem statement for RLS results from choosing the loss function V in Tikhonov regularization to be the mean squared error:
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A mean of zero is needed for finding a basis that minimizes the mean square error of the approximation of the data.
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Specifically, it is possible to furnish estimators that improve considerably upon the maximum likelihood estimate in terms of mean squared error.
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The deviation from the source is usually measured as the mean squared error, in order to maximize the PSNR video quality metric.
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On both cases, MSE ( Mean squared error ) is taken from the ANOVA conducted in the first stage of the analysis.
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The Wiener filter is designed so as to minimize the mean square error ( MMSE criteria ) which can be stated concisely as follows:
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The mean and variance ( actually, mean squared error, a distinction that will not be pursued here ) are found from the integrals
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In particular, RLS is designed to minimize the mean squared error between the predicted values and the true labels, subject to regularization.
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Such a solution achieves the lowest possible mean squared error among all unbiased methods, and is therefore the minimum variance unbiased ( MVU ) estimator.
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"' Least mean square ( LMS ) Adaptive Filters "'use the most common error measure method, the mean square error.
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