Efficient label propagation

31st International Conference on Machine Learning, ICML 2014

Volumn 3, Issue , 2014, Pages 2355-2363

  Fujiwara, Yasuhiro ^a   Lrie, Go ^b  

^a NTT Software Innovation Center   (Japan)

^b NTT CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; INVERSE PROBLEMS; LEARNING ALGORITHMS; SUPERVISED LEARNING;

AVERAGE NUMBERS; COMPUTATIONAL COSTS; GRAPH-BASED; INVERSE MATRIX; LABEL PROPAGATION; LOWER AND UPPER BOUNDS; OPTIMAL LABELING; SEMI- SUPERVISED LEARNING;

ITERATIVE METHODS;

EID: 84919924897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Alexandrescu, Andrei and Kirchhoff, Katrin. Graph-based Learning for Phonetic Classification. In ASRU, pp. 359- 364, 2007.
2. Belkin, Mikhail, Niyogi, Partha, and Sindhwani, Vikas. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
3. Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2007.
4. Chapelle, Olivier, Scholkopf, Bernhard, and Zien, Alexander. Semi-Supervised Learning. The MIT Press, 2010.
5. Chen, Jie, ren Fang, Haw, and Saad, Yousef. Fast Approximate/cNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection. Journal of Machine Learning Research, 10:1989-2012, 2009.
6. Connor, Michael and Kumar, Piyush. Fast Construction of k-Nearest Neighbor Graphs for Point Clouds. IEEE Trans. Vis. Comput. Graph., 16(4):599-608, 2010.
7. Dong, Wei, Charikar, Moses, and Li, Kai. Efficient k- Nearest Neighbor Graph Construction for Generic Similarity Measures. In WWW, pp. 577-586, 2011.
8. Elhamifar, Ehsan and Vidal, Rene. Sparse Manifold Clustering and Embedding. In NIPS, pp. 55-63, 2011.
9. Fergus, Rob, Weiss, Yair, and Torralba, Antonio. Semi- supervised Seaming in Gigantic Image Collections. In NIPS, pp. 522-530, 2009.
10. Golub, Gene H. and Loan, Charles F. Van. Matrix Computations. Johns Hopkins University Press, 2012.
11. Gunderson, David S. Handbook of Mathematical Induction: Theory and Applications. Chapman and Hall/CRC, 2010.
12. Kumar, Sanjiv, Mohri, Mehryar, and Talwalkar, Ameet. Sampling Techniques for the Nystrom Method. In AIS- TATS, pp. 304-311, 2009.
13. Roweis, Sam T. and Saul, Lawrence K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290:2323-2326, 2000.
14. Subramanya, Amarnag and Bilmes, Jeff A. En- tropic Graph Regularization in Non-parametric Semi- supervised Classification. In NIPS, pp. 1803-1811, 2009.
15. Talwalkar, Ameet, Kumar, Sanjiv, and Rowley, Henry A. Large-scale Manifold Learning. In CVPR, 2008.
16. von Luxburg, Ulrike. A Tutorial on Spectral Clustering. Statistics and Computing, 17(4):395-426, 2007.
17. Wang, Fei and Zhang, Changshui. Label Propagation through Linear Neighborhoods. IEEE Trans. Knowl. Data Eng., 20(1):55-67, 2008.
18. Xu, Bin, Bu, Jiajun, Chen, Chun, Cai, Deng, He, Xiaofei, Liu, Wei, and Luo, Jiebo. Efficient Manifold Ranking for Image Retrieval. In SIGIR, pp. 525-534, 2011.
19. Yu, Kai and Yu, Shipeng. Block wise Supervised Inference on Large Graphs. In Proc. of the 22nd ICML Workshop on Learning, 2005.
20. Zhou, Dengyong, Bousquet, Olivier, Lai, Thomas Navin, Weston, Jason, and Scholkopf, Bernhard. Learning with Local and Global Consistency. In NIPS, 2003.
21. Zhu, Xiaojin, Ghahramani, Zoubin, and Lafferty, John D. Semi-supervised Learning Using Gaussian Fields and Harmonic Functions. In ICML, pp. 912-919, 2003.