Bayesian supervised dictionary learning
Behnam Babagholami-Mohamadabadi, Amin Jourabloo, Mohammadreza Zolfaghari, Mohammad Taghi Manzuri · 2013
This paper proposes a novel Bayesian method for the dictionary learning (DL) based classification using Beta-Bernoulli process. We utilize this non-parametric Bayesian technique to learn jointly the sparse codes, the dictionary, and the classifier together. Existing DL based classification approaches only offer point estimation of the dictionary, the sparse codes, and the classifier and can therefore be unreliable when the number of training examples is small. This paper presents a Bayesian framework for DL based classification that estimates a posterior distribution for the sparse codes, the dictionary, and the classifier from labeled training data. We also develop a Variational Bayes (VB) algorithm to compute the posterior distribution of the parameters which allows the proposed model to be applicable to large scale datasets. Experiments in classification demonstrate that the proposed framework achieves higher classification accuracy than state-of-the-art DL based classification algorithms.