Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 64, Issue 14, Pages 3790-3805Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2016.2550016
Keywords
Sparse codes auto-extractor; embedding learning; dictionary learning; feature representation; joint classification
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Funding
- National Natural Science Foundation of China [61402310, 61373093]
- Natural Science Foundation of Jiangsu Higher Education Institutions of China [15KJA520002]
- Postdoctoral Science Foundation of China [2015M580462]
- Postdoctoral Science Foundation of Jiangsu Province of China [1501091B]
- Natural Science Foundation of Jiangsu Province of China [BK20140008, BK20141195]
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In this paper, we discuss the sparse codes auto-extractor based classification. A joint label consistent embedding and dictionary learning approach is proposed for delivering a linear sparse codes auto-extractor and a multi-class classifier by simultaneously minimizing the sparse reconstruction, discriminative sparse-code, code approximation and classification errors. The auto-extractor is characterized with a projection that bridges signals with sparse codes by learning special features from input signals for characterizing sparse codes. The classifier is trained based on extracted sparse codes directly. In our setting, the performance of the classifier depends on the discriminability of sparse codes, and the representation power of the extractor depends on the discriminability of input sparse codes, so we incorporate label information into the dictionary learning to enhance the discriminability of sparse codes. So, for inductive classification, our model forms an integration process from test signals to sparse codes and finally to assigned labels, which is essentially different from existing sparse coding based approaches that involve an extra sparse reconstruction with the trained dictionary for each test signal. Remarkable results are obtained by our model compared with other state-of-the-arts.
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