Abstract
We propose to jointly learn a Discriminative Bayesian
dictionary along a linear classifier using coupled BetaBernoulli Processes. Our representation model uses separate base measures for the dictionary and the classifier, but
associates them to the class-specific training data using the
same Bernoulli distributions. The Bernoulli distributions
control the frequency with which the factors (e.g. dictionary
atoms) are used in data representations, and they are inferred while accounting for the class labels in our approach.
To further encourage discrimination in the dictionary, our
model uses separate (sets of) Bernoulli distributions to represent data from different classes. Our approach adaptively
learns the association between the dictionary atoms and the
class labels while tailoring the classifier to this relation with
a joint inference over the dictionary and the classifier. Once
a test sample is represented over the dictionary, its representation is accurately labeled by the classifier due to the
strong coupling between the dictionary and the classifier.
We derive the Gibbs Sampling equations for our joint representation model and test our approach for face, object,
scene and action recognition to establish its effectiveness