Abstract
Graph-based Semi-Supervised Learning (SSL) has been an active topic in machine learning for about a decade. It is well-known that how to construct the graph is the central concern in recent work since an efficient graph structure can significantly boost the final performance. In this paper, we present a review on several different graphs for graph-based SSL at first. And then, we conduct a series of experiments on benchmark data sets in order to give a comprehensive evaluation on the advantageous and shortcomings for each of them. Experimental results shown that: a) when data lie on independent subspaces and the number of labeled data is enough, the low-rank representation based method performs best, and b) in the majority cases, the local sparse representation based method performs best, especially when the number of labeled data is few.
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References
Szummer, M., Jaakkola, T.: Partially labeled classification with markov random walks, vol. 14. MIT Press (2001)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7, 2399–2434 (2006)
Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, pp. 912–919 (2003)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol. 16, pp. 321–328. MIT Press (2004)
Bengio, Y., Delalleau, O., Le Roux, N.: Label propagation and quadratic criterion. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 193–216. MIT Press (2006)
Zhou, D., Schölkopf, B., Hofmann, T.: Semisupervised learning on directed graphs. In: NIPS (2005)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering 20(1), 55–67 (2008)
Yan, S., Wang, H.: Semi-supervised learning by sparse representation. In: SIAM International Conference on Data Mining (2009)
Cheng, H., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: ICCV (2009)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Saul, L.K., Roweis, S.T.: Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4, 119–155 (2003)
Wang, F., Zhang, C., Shen, H., Wang, J.: Semi-supervised classification using linear neighborhood propagation. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 160–167 (2006)
Li, C.G., Guo, J. Zhang, H.G.: Local sparse representation based classification. In: ICPR (2010)
Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: ICML (2010)
Elhamifar, E., Vidal, R.: Sparse subspce clustering. In: CVPR (2009)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)
Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence 27(5), 684–698 (2005)
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Li, Cg., Qi, X., Guo, J., Xiao, B. (2012). An Evaluation on Different Graphs for Semi-supervised Learning. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_8
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DOI: https://doi.org/10.1007/978-3-642-31919-8_8
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