Minimum Mean Square Distance Estimation of a Subspace

Minimum mean square estimation and neural networks

Neural networks for estimation, such as the multilayer perceptron (MLP) and functional link net (FLN), are shown to approximate the minimum mean square estimator rather than the maximum likelihood estimator or others. Cramer-Rao maximum a posteriori lower bounds on estimation error can therefore be used to approximately bound network training error, when a statistical signal model is available ...

Rate-distortion function via minimum mean square error estimation

We derive a simple general parametric representation of the rate–distortion function of a memoryless source, where both the rate and the distortion are given by integrals whose integrands include the minimum mean square error (MMSE) of the distortion ∆ = d(X, Y ) based on the source symbol X , with respect to a certain joint distribution of these two random variables. At first glance, these rel...

Mean Square Estimation

The problem of parameter estimation in linear model is pervasive in signal processing and communication applications. It is often common to restrict attention to linear estimators, which simplifies the implementation as well as the mathematical derivations. The simplest design scenario is when the second order statistics of the parameters to be estimated are known and it is desirable to minimiz...

Root Mean Square Minimum Distance: a Quality Metric for Localization Nanoscopy Imaging

A localization algorithm in optical localization nanoscopy plays an important role in obtaining a highquality image. By challenging 2D synthetic nanoscopy data, performances of 32 localization software packages were recently evaluated. The challenge has been advanced to focus on 3D imaging and become an open public online challenge that up to now has drawn 84 participant packages. A universal a...

Note on a new computational data smoothing procedure suggested by minimum mean square error estimation