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Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation
| Citations: | 4 - 2 self |
BibTeX
@MISC{Zhang_onlinesemi-supervised,
author = {Guangxiao Zhang and Zhuolin Jiang and Larry S. Davis},
title = {Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation},
year = {}
}
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Abstract
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. 1
Keyphrases
online semi-supervised discriminative dictionary learning sparse representation generalization error online semi-supervised dictionary learning algorithm objective function unlabeled data sparse code predictive linear classification task classification error unseen data discriminative power labeled set dictionary representative object category recognition application online dictionary learning input signal discriminative sparse-code error probabilistic model reconstruction error single dictionary experimental result
