Computational Statistics & Data Analysis
Initializing EM using the properties of its trajectories in Gaussian mixtures
Statistics and Computing
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Acceleration schemes with application to the EM algorithm
Computational Statistics & Data Analysis
Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter
Computational Statistics & Data Analysis
Acceleration of the EM algorithm: P-EM versus epsilon algorithm
Computational Statistics & Data Analysis
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A new acceleration scheme for optimization procedures is defined through geometric considerations and applied to the EM algorithm. In many cases it is able to circumvent the problem of stagnation. No modification of the original algorithm is required. It is simply used as a software component. Thus the new scheme can be easily implemented to accelerate a fixed point algorithm maximizing some objective function. Some practical examples and simulations are presented to show its ability to accelerate EM-type algorithms converging slowly.