A general graph-based semi-supervised learning with novel class discovery

Neural Computing and Applications

Volumn 19, Issue 4, 2010, Pages 549-555

  Nie, Feiping ^a   Xiang, Shiming ^b   Liu, Yun ^c   Zhang, Changshui ^a  

^a TSINGHUA UNIVERSITY   (China)

^b INSTITUTE OF AUTOMATION   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Normalized weights; Novel class discovery; Pattern recognition; Semi supervised learning

Indexed keywords

EID: 77952807496     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-009-0305-8     Document Type: Article

References (16)

1. Zhu XJ (2006) Semi-supervised learning literature survey, Technical Report Computer Sciences 1530, University of Wisconsin-Madison.
2. 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.
3. Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: ICML, pp 19-26.
4. Joachims T (1999) Transductive inference for text classification using support vector machines. In: ICML, pp 200-209.
5. Szummer M, Jaakkola T (2001) Partially labeled classification with markov random walks. In: NIPS, pp 945-952.
6. Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge.
7. Seeger M (2000) Learning with labeled and unlabeled data, Technical report, The University of Edinburgh.
8. Belkin M, Matveeva I, Niyogi P (2004) Regularization and semi-supervised learning on large graphs. In: COLT, pp 624-638.
9. Chung FRK (1997) Spectral graph theory. In: CBMS regional conference series in mathematics, No. 92, American Mathematical Society.
10. Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. In: NIPS.
11. Zhu X, Ghahramani Z, Lafferty JD (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML, pp 912-919.
12. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans PAMI 22(8): 888-905.
13. Zhou D, Schölkopf B (2004) Learning from labeled and unlabeled data using random walks. In: DAGM-symposium, pp 237-244.
14. Chapelle O, Zien A (2005) Semi-supervised classification by low density separation. In: The tenth international workshop on artificial intelligence and statistics.
15. Chapelle O, Weston J, Schölkopf B (2002) Cluster kernels for semi-supervised learning. In: NIPS, pp 585-592.
16. 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.