Abstract
A novel semi-supervised classification framework is proposed based on the label propagation using random walks on graph. To characterize this model, two classifiers, namely the lazy and single-step random walk classifiers are specifically derived. Sufficient experiments and comparison prove their universal adaptability and good performance.
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References
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern Classification. Knowledge Based Systems 8(6), 373–389 (1995)
Lovász, L.: Random Walks on Graphs: A survey. Combinatorics, Paul Erdös is Eighty, Keszthely, Hungary, vol. 2, pp. 1–46 (1993)
Zhu, X.: Semi-supervisedd Learning with Graphs. PhD thesis, Carnegie Mellon University (2005)
Zhou, D., Bousquet, O., Lalf, T.N., et al.: Learning with local and global consistency. In: Advances in Neural Information Processing System, vol. 16 (2004)
Zhou, D., Schölkopf, B.: Learning from Labeled and Unlabeled Data Using Random Walks. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 237–244. Springer, Heidelberg (2004)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. In: The 23th International Conference on Machine Learning, pp. 55–67 (2006)
Hassan, S., Mihalcea, R., Banea, C.: Random-Walk Term Weighting for Improved Text Classification. In: International Conference on Semantic Computing, pp. 242–249 (2007)
Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. Arxiv preprint arXiv:1101. 3291 (2011)
Szummer, M., Jaakkola, T.: Partially labeled classification with Markov random walk. In: Advances in Neural Information Processing Systems, vol. 14, pp. 945–952 (2002)
Xu, X.: Random Walk Learning on Graph. Nanjing University of Aeronautics and Astronautics, Nanjing (2008)
Cai, D., He, X., Han, J.: Efficient kernel discriminant analysis via spectral regression. Technical Report 2888, Department of Computer Science, University of Illinois at Urbana-Champaign (August 2007)
Cai, D., He, X., Hu, Y., Han, J., Huang, T.: Learning a spatially smooth subspace for face recognition. In: CVPR 2007 (2007)
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Xu, X., Lu, L., He, P., Pan, Z., Chen, L. (2012). Random Walk Classifier Framework on Graph. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_6
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DOI: https://doi.org/10.1007/978-3-642-33506-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
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