Accelerating EM by targeted aggressive double extrapolation

The Expectation-Maximization (EM) algorithm is one of the most popular algorithms for parameter estimation from incomplete data, but its convergence can be slow for some large-scale or complex problems. Extrapolation methods can effectively accelerate EM, but to ensure stability, the learning rate of extrapolation must be compromised. This paper describes the TJ²aEM method, a targeted extrapolation method that can extrapolate much more aggressively than competing methods without causing instability problems. We analyze its convergence properties and report experimental results.