A Variational Learning Algorithm for the Abstract Hidden Markov Model
We present a fast algorithm for learning the parameters of the abstract hidden Markov model, a type of hierarchical activity recognition model. Learning using exact inference scales poorly as the number of levels in the hierarchy increases; therefore, an approximation is required for large models. We demonstrate that variational inference is well suited to solve this problem. Not only does this technique scale. but it also offers a natural way to leverage the context specific independence properties inherent in the model via the fixed point equations. Experiments confirm that the variational approximation significantly reduces the time necessary for learning while estimating parameter values that can be used to make reliable predictions.