Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation
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- 作者：Guangxiao Zhang (20)
Zhuolin Jiang (20)
Larry S. Davis (20) - 刊名：Lecture Notes in Computer Science
- 出版年：2013
- 出版时间：2013
- 年：2013
- 卷：7724
- 期：1
- 全文大小：608KB
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Zhuolin Jiang (20)
Larry S. Davis (20)
20. University of Maryland, College Park, MD, 20742, USA - ISSN：1611-3349
文摘
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.