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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rohban, M.H., Rabiee, H.R.: Supervised neighborhood graph construction for semi-supervised classification. Pattern Recogn. 45, 1363–1372 (2012)
Zhang, F., Yang, J., Qian, J.: Nuclear norm-based 2-DPCA for extracting features from images. Proc. IEEE Trans. Neural Netw. Learn. Syst. 26(10), 2247–2260 (2015)
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the ICML, pp. 19–26 (2001)
Chapelle, O., Weston, J.: Cluster kernels for semi-supervised learning. Adv. Neural Inf. Process. Syst. 15, 15–17 (2003)
Hou, C., Nie, F., Li, X.: Joint embedding learning and sparse regression: a framework for unsupervised feature selection. Proc. IEEE Trans. Cybern. 44(6), 793–804 (2013)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. In: ICML, pp. 985–992 (2006)
Yang, Y.: L21-norm regularized discriminative feature selection for unsupervised learning. In: Proceedings of the AI, pp. 1589–1594 (2011)
Wang, J.: Locally Linear Embedding, pp. 203–220. Springer, Heidelberg (2012)
Zhang, C., Wang, S., Li, D.: Prior class dissimilarity based linear neighborhood propagation. Knowl.-Based Syst. 83, 58–65 (2015)
Nie, F., Xiang, S., Liu, Y.: A general graph-based semi-supervised learning with novel class discovery. Neural Comput. Appl. 19, 549–555 (2010)
Yang, S.Z., Hou, C.P., Nie, F.P., Wu, Y.: Unsupervised maximum margin feature selection via L2,1-norm minimization. Neural Comput. Appl. 21(7), 1791–1799 (2012)
Zhang, Z., Zhang, L., Zhao, M.B., Jiang, W.M., Liang, Y.C., Li, F.Z.: Semi-supervised image classification by nonnegative sparse neighborhood propagation. In: Proceedings of the ACM-ICMR, pp. 139–146 (2015)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with local and global consistency. Adv. Neural Inf. Process. Syst. 17(4), 321–328 (2004)
Zhang, Z., Zhang, Y., Li, F., Zhao, M., Zhang, L., Yan, S.: Discriminative Sparse Flexible Manifold Embedding with Novel Graph for Robust Visual Representation and Label Propagation. Pattern Recogn. 61, 492–510, (2017)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-46675-0_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46674-3
Online ISBN: 978-3-319-46675-0
eBook Packages: Computer ScienceComputer Science (R0)