Global linear neighborhoods for efficient label propagation

Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Volumn , Issue , 2012, Pages 863-872

  Tian, Ze ^a   Kuang, Rui ^a  

^a University of Minnesota   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); GENE EXPRESSION; GRAPHIC METHODS; LARGE DATASET; MACHINE LEARNING; SUPERVISED LEARNING;

CLASSIFICATION RESULTS; GENE EXPRESSION DATASETS; LABELED AND UNLABELED DATA; LARGE SCALE SIMULATIONS; MULTIPLICATIVE UPDATES; RECONSTRUCTION ERROR; SEMI- SUPERVISED LEARNING; SPARSE REPRESENTATION;

DATA MINING;

EID: 84880250285     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.74     Document Type: Conference Paper

References (24)

1. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems 14, pages 585-591. MIT Press, 2001.
2. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neu- ral Computation, 15(6):1373-1396, Jun 2003.
3. M. Belkin and P. Niyogi. Using manifold stucture for partially labeled classification. In S. T. S. Becker and K. Obermayer, editors, Advances in Neural Informa- Tion Processing Systems 15, pages 929-936. MIT Press, Cambridge, MA, 2003.
4. Y. Bengio, O. Delalleau, and N. Le Roux. Label Propagation and Quadratic Criterion. In O. Chapelle, B. Schölkopf, and A. Zien, editors, Semi-Supervised Learning, pages 193-216. MIT Press, 2006.
5. M. Breitenbach and G. Z. Grudic. Clustering through ranking on manifolds. In ICML, pages 73-80, 2005.
6. H. Cheng, Z. Liu, and J. Yang. Sparsity induced similarity measure for label propagation. In Computer Vision, 2009 IEEE 12th International Conference on, pages 317-324, Oct. 2009.
7. H. S. S. Daniel, D. Lee. Algorithms for non-negative matrix factorization. In Advances in Neural Informa- Tion Processing Systems 13, pages 556-562. MIT Press, 2000.
8. I. S. Dhillon, S. Mallela, and R. Kumar. A divisive information theoretic feature clustering algorithm for text classification. Journal of Machine Learning Re- search, 3:1265-1287, March 2003.
9. A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intel- ligence, 23(6):643-660, Jun 2001.
10. M. T. Landi et al. Gene Expression Signature of Cigarette Smoking and Its Role in Lung Adenocarcinoma Development and Survival. PLOS ONE, 3(2), FEB 20 2008.
11. W. Liu, J. He, and S.-F. Chang. Large graph construction for scalable semi-supervised learning. In ICML, pages 679-686, 2010.
12. W. Liu, S. Ma, D. Tao, J. Liu, and P. Liu. Semisupervised sparse metric learning using alternating linearization optimization. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pages 1139-1148, New York, NY, USA, 2010. ACM. ISBN 978-1-4503- 0055-1.
13. D. Luo, C. Ding, H. Huang, and T. Li. Non-negative laplacian embedding. In Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on, pages 337-346, Dec. 2009.
14. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290 (5500):2323+, Dec 22 2000.
15. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, Aug 2000.
16. L.-J. Su et al. Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme. BMC GE- NOMICS, 8, JUN 1 2007.
17. M. Szummer and T. Jaakkola. Partially labeled classification with markov random walks. In Advances in Neural Information Processing Systems 14, pages 945-952. MIT Press, 2001.
18. L. van't Veer et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415(6871): 530-536, Jan 31 2002.
19. F.Wang and C. Zhang. Label propagation through linear Neighborhoods. IEEE Transactions on Knowledge and Data Engineering, 20(1):55-67, Jan 2008.
20. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. In S. T. S. Becker and K. Obermayer, editors, Advances in Neural Information Pro- cessing Systems 15, pages 505-512. MIT Press, Cambridge, MA, 2003.
21. S. Yan and H. Wang. Semi-supervised learning by sparse representation. In SIAM International Confer- ence on Data Mining, pages 792-801, 2009.
22. N. Yang, Y. Sang, R. He, and X. Wang. Label propagation algorithm based on non-negative sparse representation. In K. Li, L. Jia, X. Sun, M. Fei, and G. Irwin, editors, Life System Modeling and Intelligent Computing, volume 6330 of Lecture Notes in Computer Science, pages 348-357. Springer Berlin/Heidelberg, 2010.
23. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Sys- Tems 16. MIT Press, Cambridge, MA, 2004.
24. X. Zhu, Z. Ghahramani, and J. D. Laérty. Semisupervised learning using gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.