  The expectation-maximization (EM) algorithm is a well-known iterative method
for computing maximum likelihood estimates from incomplete data. Despite its
numerous advantages, a main drawback of the EM algorithm is its frequently
observed slow convergence which often hinders the application of EM algorithms
in high-dimensional problems or in other complex settings.To address the need
for more rapidly convergent EM algorithms, we describe a new class of
acceleration schemes that build on the Anderson acceleration technique for
speeding fixed-point iterations. Our approach is effective at greatly
accelerating the convergence of EM algorithms and is automatically scalable to
high dimensional settings. Through the introduction of periodic algorithm
restarts and a damping factor, our acceleration scheme provides faster and more
robust convergence when compared to un-modified Anderson acceleration while
also improving global convergence. Crucially, our method works as an
"off-the-shelf" method in that it may be directly used to accelerate any EM
algorithm without relying on the use of any model-specific features or
insights. Through a series of simulation studies involving five representative
problems, we show that our algorithm is substantially faster than the existing
state-of-art acceleration schemes.
