Expectation-Maximization-Aided Hybrid Generalized Expectation Consistent for Sparse Signal Reconstruction

Space-Alternating Generalized Expectation-Maximization Algorithm

The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all paramete...

Space - Alternating Generalized Expectation - Maximization AlgorithmJe rey

| The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parame...

Space-alternating generalized expectation-maximization algorithm

The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all paramete...

An Improved Ordered Subsets Expectation Maximization Reconstruction

Positron emission computerized tomography (PET) based on Ordered Subsets Expectation Maximization (OS-EM) is usually used to computerized tomography, which imposes the radionuclide to emission positron. But it also has some disadvantage as costing long time and bad reconstruction quality. Filtered Back Projection (FBP) that has many advantages such as simple structure and short reconstruction t...

Expectation Maximization