Abstract
In this paper, we propose a Nuclear norm-driven Semi-Supervised Dictionary Learning (N-SSDL) approach for classification. N-SSDL incorporates the idea of the recent label consistent KSVD with the label propagation process that propagates label information from labeled data to unlabeled data via balancing the neighborhood reconstruction error and the label fitness error. To provide a more reliable distance metric for measuring the neighborhood reconstruction error, we apply the nuclear-norm that is proved to be suitable for modeling the reconstruction error, where the reconstruction coefficients are computed based on the sparsely reconstructed training data rather than original ones. Besides, we also use the robust l 2,1 -norm regularization on the label fitness error so that the measurement is robust to noise and outliers. Extensive simulations on several datasets show that N-SSDL can deliver enhanced performance over other state-of-the-arts for classification.
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Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (61402310, 61672365, 61672364 and 61373093), Major Program of Natural Science Foundation of Jiangsu Higher Education Institutions of China (15KJA520002), Special Funding of China Postdoctoral Science Foundation (2016T90494), Postdoctoral Science Foundation of China (2015M580462) and Postdoctoral Science Foundation of Jiangsu Province of China (1501091B), Natural Science Foundation of Jiangsu Province of China (BK20140008, BK20141195), and Graduate Student Innovation Project of Jiangsu Province of China (SJZZ15_0154).
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Jiang, W., Zhang, Z., Zhang, Y., Li, F. (2016). Discriminative Sparse Coding by Nuclear Norm-Driven Semi-Supervised Dictionary Learning. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_30
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DOI: https://doi.org/10.1007/978-3-319-48890-5_30
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