Abstract
We propose an enhanced label prediction method termed Transductive Classification Robust Linear Neighborhood Propagation (R-LNP). To encode the neighborhood reconstruction error more accurately, we apply the L2,1-norm that is proved to be very robust to noise for characterizing the manifold smoothing term. Since L2,1-norm can also enforce the neighborhood reconstruction error to be sparse in rows, i.e., entries of some rows are zeros. In addition, to enhance robustness in the process of modeling the difference between the initial labels and predicted ones, we also regularize the weighted L2,1-norm on the label fitting term, so the resulted measures would be more accurate. Compared with several transductive label propagation models, our proposed algorithm obtains state-of-the-art performance over extensive representation and classification experiments.
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Acknowledgments
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, SJZZ16_0236).
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Jia, L., Zhang, Z., Jiang, W. (2016). Transductive Classification by Robust Linear Neighborhood Propagation. 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_29
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DOI: https://doi.org/10.1007/978-3-319-48890-5_29
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