Neural Expectation Maximization

An Expectation-Maximization Approach to Nonlinear Component Analysis

Comparison of the Expectation Maximization Algorithm and an Artificial Neural Network for Automated Bone Segmentation

Comparison of the Expectation Maximization Algorithm and an Artificial Neural Network for Automated Bone Segmentation Austin Ramme4, Vincent Magnotta1,2, Nicole M. Grosland2,3 1Radiology, University of Iowa, Iowa City, IA; 2Biomedical Engineering, University of Iowa, Iowa City, IA; 3Center for Computer Aided Design, The University of Iowa, Iowa City, IA; 4Carver College of Medicine, The Univers...

Statistical analysis of neural data: The expectation-maximization (EM) algorithm

Using Expectation-Maximization for Reinforcement Learning

We discuss Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-...

Noise-enhanced clustering and competitive learning algorithms

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up conve...