In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM) ). Our experiment...

Iterative maximum-likelihood expectation maximization and ordered-subset expectation maximization algorithms are excellent for image reconstruction and usually provide better images than filtered backprojection (FBP). Recently, an FBP algorithm able to incorporate noise weighting during reconstruction was developed. This paper compares the performance of the noise-weighted FBP algorithm and the...

The multi-reference alignment (MRA) problem entails estimating an image from multiple noisy and rotated copies of itself. If the noise level is low, one can reconstruct by missing rotations, aligning images, averaging out noise. While accurate rotation estimation impossible if high, rotations still be approximated, thus provide indispensable information. In particular, learning approximation er...