Robust self-tuning semi-supervised learning

Neurocomputing

Volumn 70, Issue 16-18, 2007, Pages 2931-2939

  Wang, Fei ^a   Zhang, Changshui ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Graph; Semi supervised learning

Indexed keywords

GRAPHIC METHODS; ROBUST CONTROL; SELF TUNING CONTROL SYSTEMS; SIGNAL ENCODING;

ROBUST SELF-TUNING GRAPH; SEMI-SUPERVISED LEARNING;

SUPERVISED LEARNING;

ARTICLE; AUTOMATION; CONTROLLED STUDY; INFORMATION PROCESSING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SENSITIVITY ANALYSIS;

EID: 34548150972     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.11.004     Document Type: Article

References (19)

1. M. Belkin, I. Matveeva, P. Niyogi, Regularization and semi-supervised learning on large graphs, in: Proceedings of the 17th COLT, 2004.
2. Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15 (2003) 1373-1396
3. Belkin M., and Niyogi P. Semi-supervised learning on Riemannian manifolds. Mach. Learn. 56 (2004) 209-239
4. Y. Bengio, J. Paiement, P. Vincent, Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering, in: NIPS03, 2003.
5. Chang H., and Yeung D.Y. Robust locally linear embedding. Pattern Recognition 39 6 (2006) 1053-1065
6. O. Chapelle, B. Schoölkopf, A. Zien (Eds.), Semi-supervised Learning, MIT Press, Cambridge, MA, 2006.
7. O. Chapelle, J. Weston, B. Schölkopf, Cluster kernels for semi-supervised learning, NIPS 15, 2003.
8. Choi H., and Choi S. Kernel Isomap on noisy manifold. Proceedings of the IEEE International Conference on Development and Learning (ICDL). Osaka, Japan, July 19-21 (2005) 208-213
9. De la Torre F., and Black M.J. A framework for robust subspace learning. Int. J. Comput. Vision 54 1-3 (2003) 117-142
10. O. Delalleu, Y. Bengio, N. Le Roux, Non-parametric function induction in semi-supervised learning, in: Proceedings of the 10th AISTATS, 2005.
11. Kakutani S. Markov process and the Dirichlet problem. Proc. Jpn. Acad. 21 (1945) 227-233
12. Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
13. Schölkopf B., and Smola A.J. Learning with Kernels (2002), MIT Press, Cambridge, MA
14. M. Szummer, T. Jaakkola, Partially labeled classification with Markov random walks. Adv. Neural Inf. Process. Syst. 14 (2002).
15. F. Wang, C. Zhang, Label propagation through linear neighborhoods, in: Proceedings of the 23rd ICML, 2006, to appear.
16. Zhou D., Bousquet O., Lal T.N., Weston J., and Schölkopf B. Learning with local and global consistency. NIPS 16 (2004)
17. D. Zhou, B. Schölkopf, Learning from labeled and unlabeled data using random walks, in: Pattern Recognition, Proceedings of the 26th DAGM, 2004.
18. X. Zhu, Semi-supervised learning literature survey. Computer Sciences Technical Report 1530, University of Wisconsin-Madison, 2005.
19. X. Zhu, Z. Ghahramani, J. Lafferty, Semi-supervised learning using Gaussian fields and harmonic functions, in: Proceedings of the 20th ICML, 2003.