Semisupervised kernel matrix learning by kernel propagation

IEEE Transactions on Neural Networks

Volumn 21, Issue 11, 2010, Pages 1831-1841

  Hu, Enliang ^a   Chen, Songcan ^b   Zhang, Daoqiang ^b   Yin, Xuesong ^c  

^a YUNNAN NORMAL UNIVERSITY   (China)

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^c Zhejiang Radio and TV University   (China)

Author keywords

Kernel propagation; out of sample extension; pairwise constraint; seed kernel matrix learning; semidefinite programming

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER LANGUAGE; COMPUTER PROGRAM; MATHEMATICAL COMPUTING; NORMAL DISTRIBUTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; MATHEMATICAL COMPUTING; NEURAL NETWORKS (COMPUTER); NORMAL DISTRIBUTION; PROGRAMMING LANGUAGES; SOFTWARE DESIGN;

EID: 78149357397     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2076301     Document Type: Article

References (28)

1. X. J. Zhu, "Semi-supervised learning literature survey," Dept. Comput. Sci., Univ. Wisconsin, Madison, Tech. Rep. 1530, 2005.
2. F. Aiolli, G. Da San Martino, M. Hagenbuchner, and A. Sperduti, "Learning nonsparse kernels by self-organizing maps for structured data," IEEE Trans. Neural Netw., vol. 20, no. 12, pp. 1938-1949, Dec. 2009.
3. K. Wagsaff, C. Cardie, S. Rogers, and S. Schroedl, "Constrained K-means clustering with background knowledge," in Proc. 18th Int. Conf. Mach. Learn., Williamstown, MA, Jun. 2001, pp. 577-584.
4. A. Smola and R. Kondor, "Kernels and regularization on graphs," in Proc. 16th Annu. Conf. Comput. Learn. Theory 7th Kernel Workshop, Washington D.C., Aug. 2003, pp. 144-158.
5. T. Zhang and R. K. Ando, "Analysis of spectral kernel design based semi-supervised learning," in Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, and J. Platt, Eds. Cambridge, MA: MIT Press, 2006, pp. 1601-1608.
6. O. Chapelle, J. Weston, and B. Schölkopf, "Cluster kernels for semi-supervised learning," in Advances in Neural Information Processing Systems 15, S. Becker, S. Thrun, and K. Obermayer, Eds. Cambridge, MA: MIT Press, 2003, pp. 585-592.
7. R. I. Kondor and J. Lafferty, "Diffusion kernels on graphs and other discrete input spaces," in Proc. 19th Int. Conf. Mach. Learn., Sydney, Australia, Jul. 2002, pp. 315-322.
8. X. J. Zhu, J. Lafferty, and Z. Ghahramani, "Semi-supervised learning: From Gaussian fields to Gaussian processes," Dept. Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, Tech. Rep. CMU-CS-03-175, 2003.
9. X. J. Zhu, J. Kandola, J. Lafferty, and Z. Ghahramani, "Graph kernels by spectral transforms," in Semi-Supervised Learning, O. Chapelle, B. Schölkopf, and A. Zien, Eds. Cambridge, MA: MIT Press, 2006, pp. 277-289.
10. Z. Li, J. Liu, and X. Tang, "Pairwise constraint propagation by semi-definite programming for semi-supervised classification," in Proc. 25th Int. Conf. Mach. Learn., Helsinki, Finland, Jun. 2008, pp. 576-583.
11. P. Sun and X. Yao, "Sparse approximation through boosting for learning large scale kernel machines," IEEE Trans. Neural Netw., vol. 21, no. 6, pp. 883-894, Jun. 2010.
12. K. Sinha and M. Belkin, "Semi-supervised learning using sparse eigen-function bases," in Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, Eds. Cambridge, MA: MIT Press, 2009, pp. 1687-1695.
13. Y. Bengio, O. Delalleau, and N. Le Roux, "Label propagation and quadratic criterion," in Semi-Supervised Learning, O. Chapelle, B. Schölkopf, and A. Zien, Eds. Cambridge, MA: MIT Press, 2006, pp. 193-215.
14. X. J. Zhu, Z. Ghahramani, and J. Lafferty, "Semi-supervised learning using Gaussian fields and harmonic functions," in Proc. 20th Int. Conf. Mach. Learn., Washington D.C., Aug. 2003, pp. 912-919.
15. F. Wang and C. Zhang, "Label propagation through linear neighborhoods," in Proc. 23rd Int. Conf. Mach. Learn., Pittsburgh, PA, Jun. 2006, pp. 985-992.
16. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf, "Ranking on data manifold," in Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004, pp. 169-176.
17. Y. Bengio, O. Delalleau, and N. L. Roux, "A discussion of semi-supervised learning and transductive," in Semi-Supervised Learning, O. Chapelle, B. Schölkopf, and A. Zien, Eds. Cambridge, MA: MIT Press, 2006, pp. 473-478.
18. Y. Bengio, J.-F. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet, "Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering," in Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004, pp. 177-184.
19. D. Zhang, S. Chen, Z.-H. Zhou, and Q. Yang, "Constraint projections for ensemble learning," in Proc. 23rd AAAI Conf. Artif. Intell., Chicago, IL, Jul. 2008, pp. 758-763.
20. W. Tang, H. Xiong, S. Zhong, and J. Wu, "Enhancing semi-supervised clustering: A feature projection perspective," in Proc. 13th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, San Jose, CA, Aug. 2007, pp. 707-716.
21. K. Q. Weinberger, B. D. Packer, and L. K. Saul, "Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization," in Proc. 10th Int. Workshop Artif. Intell. Statist., Savannah, GA, Jan. 2005, pp. 381-388.
22. J. He, M. Li, H.-J. Zhang, H. Tong, and C. Zhang, "Generalized manifold-ranking-based image retrieval," IEEE Trans. Image Process., vol. 15, no. 10, pp. 3170-3177, Oct. 2006.
23. M. Breitenbach and G. Grudic, "Clustering through ranking on manifolds," in Proc. 22nd Int. Conf. Mach. Learn., Bonn, Germany, Aug. 2005, pp. 73-80.
24. A. Strehl and J. Ghosh, "Cluster ensembles\a knowledge reuse framework for combining multiple partitions," J. Mach. Learn. Res., vol. 3, no. 3, pp. 583-617, Mar. 2003.
25. J. He, M. Li, H.-J. Zhang, H. Tong, and C. Zhang, "Manifold-ranking based image retrieval," in Proc. 12th ACM Int. Conf. Multimedia, New York, Oct. 2004, pp. 9-16.
26. B. Borchers, "CSDP, a C library for semidefinite programming," Optim. Methods Softw., vol. 11, nos. 1-4, pp. 613-623, 1999.
27. S. Kamvar, D. Klein, and C. Manning, "Spectral learning," in Proc. 18th Int. Joint Conf. Artif. Intell., Acapulco, Mexico, Aug. 2003, pp. 561-566.
28. B. Kulis, S. Basu, I. Dhillon, and R. Mooney, "Semi-supervised graph clustering: A kernel approach," in Proc. 22nd Int. Conf. Mach. Learn., Bonn, Germany, Aug. 2005, pp. 457-464.