Fast graph construction using auction algorithm

Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

Volumn , Issue , 2012, Pages 873-882

  Wang, Jun ^a   Xia, Yinglong ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COSTS; DATA REPRESENTATIONS; GRAPH CONSTRUCTION; GRAPH-CONSTRUCTION METHOD; LEARNING PARADIGMS; RELATIONAL LEARNING; SUPERVISED CLASSIFICATION; UNSUPERVISED CLUSTERING;

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; LEARNING SYSTEMS; SCALABILITY; SOCIAL NETWORKING (ONLINE);

GRAPH THEORY;

EID: 84885964654     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. A. Azran. The rendezvous algorithm: Multiclass semi-supervised learning with markov random walks. In Z. Ghahramani, editor, Proceedings of the International Conference on Machine learning, pages 49-56, Corvalis, Oregon, USA, June 2007.
2. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
3. D. Bertsekas and D. Castanon. A forward/reverse auction algorithm for asymmetric assignment problems. Computational Optimization and Applications, 1:277-297, 1993.
4. D. Bertsekas and D. Castanon. Parallel synchronous and asynchronous implementations of the auction algorithm. Parallel Computing, 17(6-7):707-732, 1991.
5. M. Breitenbach and G. Grudic. Clustering through ranking on manifolds. In Proceedings of the 22nd international conference on Machine learning, pages 73-80, 2005.
6. M. Breitenbach and G. Grudic. Clustering through ranking on manifolds. In Proceedings of the 22nd international conference on Machine learning, pages 73-80, 2005.
7. T. A. Davis and Y. Hu. The university of florida sparse matrix collection. ACM Trans. Math. Softw., 38(1):1:1-1:25, 2011.
8. H. Gabow. An efficient reduction technique for degree-constrained subgraph and bidirected network flow problems. In Proceedings of the fifteenth annual ACM symposium on Theory of computing, pages 448-456, 1983.
9. B. Huang and T. Jebara. Loopy belief propagation for bipartite maximum weight b-matching. In Int. Workshop on Artificial Intelligence and Statistics, 2007.
10. B. Huang and T. Jebara. Fast b-matching via sufficient selection belief propagation. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.
11. T. Jebara and V. Shchogolev. B-matching for spectral clustering. In Proceedings of the European Conference on Machine Learning, pages 679-686, 2006.
12. T. Jebara, J. Wang, and S.-F. Chang. Graph construction and b-matching for semi-supervised learning. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 441-448, Montreal, Quebec, Canada, June 2009.
13. D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, and M. Van Alstyne. Computational social science. Science, 323(5915):721-723, 6 February 2009.
14. W. Liu, J. He, and S. Chang. Large graph construction for scalable semi-supervised learning. In Proceedings of the 27th International Conference on Machine Learning, pages 679-686, 2010.
15. M. Maier, U. von Luxburg, and M. Hein. Influence of graph construction on graph-based clustering measures. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in neural information processing systems, volume 22, pages 1025-1032. MIT Press, 2009.
16. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2:849-856, 2002.
17. L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web, 1999.
18. C. Papadimitriou and K. Steiglitz. Combinatorial optimization: algorithms and complexity. Dover Pubns, 1998.
19. E. J. Riedy and J. Demmel. Sparse data structures for weighted bipartite matching. In SIAM Parallel Processing for Scientific Computing, pages 1-2, 2004.
20. J. Shi and J. Malik. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):888-905, 2000.
21. X. Shi, J. Leskovec, and D. McFarland. Citing for high impact. In Proceedings of the 10th annual joint conference on Digital libraries, pages 49-58, 2010.
22. M. Szummer and T. Jaakkola. Partially labeled classification with markov random walks. In Advances in Neural Information Processing Systems 17, volume 14, pages 945-952. MIT Press, Cambridge, MA, 2002.
23. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Scholkopf. Learning with local and global consistency. In S. Thrun, L. Saul, and B. Scholkopf, editors, Advances in Neural Information Processing Systems, volume 16, pages 321-328. MIT Press, Cambridge, MA, 2004.
24. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Scholkopf. Ranking on data manifolds. Advances in neural information processing systems, 16:169-176, 2004.
25. X. Zhu, Z. Ghahramani, and J. Lafferty. Semisupervised learning using gaussian fields and harmonic functions. In Proceedings of International Conference on Machine Learning, pages 912-919, 2003.