Abstract
In this paper, we propose a new transductive label propagation method, Nuclear-norm based Transductive Label Propagation (N-TLP). To encode the neighborhood reconstruction error more accurately and reliably, we use the nuclear norm that has been proved to be more robust to noise and more suitable to model the reconstruction error than both L1-norm or Frobenius norm for characterizing the manifold smoothing degree. During the optimizations, the Nuclear-norm based reconstruction error term is transformed into the Frobenius norm based one for pursuing the solution. To enhance the robustness in the process of encoding the difference between initial labels and predicted ones, we propose to use a weighted L2,1-norm regularization on the label fitness error so that the resulted measurement would be more accurate. Promising results on several benchmark datasets are delivered by our N-TLP compared with several other related methods.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (61402310,61672365, 61373093, 61672364), 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), Postdoctoral Science Foundation of Jiangsu Province of China (1501091B), Natural Science Foundation of Jiangsu Province of China (BK20140008 and BK20141195), and the Graduate Student Innovation Project of Jiangsu Province of China (SJZZ16_0236).
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Jia, L., Zhang, Z., Zhang, Y. (2016). Semi-supervised Classification by Nuclear-Norm Based Transductive Label Propagation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_41
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DOI: https://doi.org/10.1007/978-3-319-46675-0_41
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