Semi-supervised classification using multiple clusterings
- 61 Downloads
Graph determines the performance of graph-based semi-supervised classification. In this paper, we investigate how to construct a graph from multiple clusterings and propose a method called Semi-Supervised Classification using Multiple Clusterings (SSCMC in short). SSCMC firstly projects original samples into different random subspaces and performs clustering on the projected samples. Then, it constructs a graph by setting an edge between two samples if these two samples are clustered in the same cluster for each clustering. Next, it combines these graphs into a composite graph and incorporates the resulting composite graph with a graph-based semi-supervised classifier based on local and global consistency. Our experimental results on two publicly available facial images show that SSCMC not only achieves higher accuracy than other related methods, but also is robust to input parameters.
Keywordssemi-supervised classification multiple clusterings composite graph
Unable to display preview. Download preview PDF.
- 1.X. Zhu, “Semi-supervised learning literature survey,” Tech. Rep. (Department of Computer Science, Univ. of Wisconsin, Madison, 2008), no. 1530.Google Scholar
- 3.X. J. Zhu, Z. Ghahramani, and J. Lafferty, “Semisupervised learning using Gaussian fields and harmonic functions,” in Proc. 20th Int. Conf. on Machine Learning (Washington, 2003), pp. 912–919.Google Scholar
- 4.D. Y. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Scholköpf, “Learning with local and global consistency,” in Proc. Advances in Neural Information Processing Systems Conf. (Vancouver, 2003), pp. 321–328.Google Scholar
- 6.W. Liu and S. Chang, “Robust multi-class transductive learning with graphs,” in Proc. 19th IEEE Conf. on Computer Vision and Pattern Recognition (Miami, 2009), pp. 381–388.Google Scholar
- 11.G. X. Yu, H. Rangwala, C. Domeniconi, G. J. Zhang, and Z. L. Zhang, “Protein function prediction by integrating multiple kernels,” in Proc. 23rd Int. Joint Conf. on Artificial Intelligence (Beijing, 2013), pp. 1869–1875.Google Scholar
- 14.X. H. Fu, X. C. Zou, X. T. Zou, and G. X. Yu, “Semisupervised dimensionality reduction based on composite graph,” J. Comput. Inf. Syst. 10 (19), 8429–8437 (2014).Google Scholar
- 15.M. Maier, U. V. Luxburg, and M. Hein, “Influence of graph construction on graph-based clustering measures,” in Proc. 21st Conf. Advances Neural Information Processing Systems (Vancouver, 2008), pp. 1025–1032.Google Scholar
- 17.A. Fred and A. Jain, “Combing multiple clusterings using evidence accumulation,” IEEE Trans. Pattern Anal. Mach. Intell. 27 (6), 442–451(2002).Google Scholar
- 18.S. Samaria and A. C. Harter, “Parameterisation of a stochastic model for human face identification,” in Proc. 2nd IEEE Workshop on Applications of Computer Vision (Sarasota, FL, 1994), pp. 138–142.Google Scholar