Abstract
In this paper, we propose a general graph-based semi-supervised learning algorithm. The core idea of our algorithm is to not only achieve the goal of semi-supervised learning, but also to discover the latent novel class in the data, which may be unlabeled by the user. Based on the normalized weights evaluated on data graph, our algorithm is able to output the probabilities of data points belonging to the labeled classes or the novel class. We also give the theoretical interpretations for the algorithm from three viewpoints on graph, i.e., regularization framework, label propagation, and Markov random walks. Experiments on toy examples and several benchmark datasets illustrate the effectiveness of our algorithm.
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References
Zhu XJ (2006) Semi-supervised learning literature survey, Technical Report Computer Sciences 1530, University of Wisconsin-Madison
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: ICML, pp 19–26
Joachims T (1999) Transductive inference for text classification using support vector machines. In: ICML, pp 200–209
Szummer M, Jaakkola T (2001) Partially labeled classification with markov random walks. In: NIPS, pp 945–952
Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge
Seeger M (2000) Learning with labeled and unlabeled data, Technical report, The University of Edinburgh
Belkin M, Matveeva I, Niyogi P (2004) Regularization and semi-supervised learning on large graphs. In: COLT, pp 624–638
Chung FRK (1997) Spectral graph theory. In: CBMS regional conference series in mathematics, No. 92, American Mathematical Society
Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: NIPS
Zhu X, Ghahramani Z, Lafferty JD (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML, pp 912–919
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans PAMI 22(8):888–905
Zhou D, Schölkopf B (2004) Learning from labeled and unlabeled data using random walks. In: DAGM-symposium, pp 237–244
Chapelle O, Zien A (2005) Semi-supervised classification by low density separation. In: The tenth international workshop on artificial intelligence and statistics
Chapelle O, Weston J, Schölkopf B (2002) Cluster kernels for semi-supervised learning. In: NIPS, pp 585–592
Spielman DA, Teng SH (2004) Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In: Annual ACM symposium on theory of computing
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Nie, F., Xiang, S., Liu, Y. et al. A general graph-based semi-supervised learning with novel class discovery. Neural Comput & Applic 19, 549–555 (2010). https://doi.org/10.1007/s00521-009-0305-8
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DOI: https://doi.org/10.1007/s00521-009-0305-8