  We present a new model for time series classification, called the hidden-unit
logistic model, that uses binary stochastic hidden units to model latent
structure in the data. The hidden units are connected in a chain structure that
models temporal dependencies in the data. Compared to the prior models for time
series classification such as the hidden conditional random field, our model
can model very complex decision boundaries because the number of latent states
grows exponentially with the number of hidden units. We demonstrate the strong
performance of our model in experiments on a variety of (computer vision)
tasks, including handwritten character recognition, speech recognition, facial
expression, and action recognition. We also present a state-of-the-art system
for facial action unit detection based on the hidden-unit logistic model.
