Abstract. We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary's representative and discriminative power. In addition, we propose a probabilistic model over the sparse codes of input signals, which allows us to expand the labeled set. As a consequence, the dictionary and the classifier learned from the enlarged labeled set yield lower generalization error on unseen data. Our approach learns a single dictionary and a predictive linear classifier jointly. Experimental results demonstrate the effectiveness of our approach in face and object category recognition applications.
Zhang et al. REF proposed an online semi-supervised dictionary learning framework which integrated the reconstruction error of both labeled and unlabeled data, label consistency, and the classification error into an objective function.
Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation
ACCV
Computer Science