Learning a propagable graph for semisupervised learning: Classification and regression

IEEE Transactions on Knowledge and Data Engineering

Volumn 24, Issue 1, 2012, Pages 114-126

  Ni, Bingbing ^a   Yan, Shuicheng ^b   Kassim, Ashraf ^b  

^a ADVANCED DIGITAL SCIENCES CENTER   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

and similarity graph; feature extraction; graph based learning; Semi supervised learning

Indexed keywords

AND SIMILARITY GRAPH; CLASSIFICATION TASKS; DATA LABELS; DATA MINING TASKS; FEATURE REPRESENTATION; GRAPH-BASED LEARNING; HANDWRITTEN DIGIT RECOGNITION; HARMONIC SYSTEM; HUMAN AGE ESTIMATION; LABEL PROPAGATION; LEARNING FRAMEWORKS; LEARNING PROCESS; MULTICLASS CLASSIFICATION PROBLEMS; SEMI-SUPERVISED LEARNING; TOY DATA; UNIFIED FRAMEWORK;

CHARACTER RECOGNITION; FACE RECOGNITION; FEATURE EXTRACTION; ONTOLOGY; OPTIMIZATION; REGRESSION ANALYSIS; SUPERVISED LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 82155163866     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2010.209     Document Type: Article

References (30)

1. The Fg-Net Aging Database: http://sting.cycollege.ac.cy/ ~alanitis/fgnetaging.html, 2009.
2. A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, 'Learning Distance Functions Using Equivalence Relations,' Proc. Int'l Conf. Machine Learning, pp. 11-18, 2003.
3. P. Belhumeur, J. Hespanda, and D. Kiregeman, 'Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997. (Pubitemid 127762263)
4. M. Belkin, I. Matveeva, and P. Niyogi, 'Regularization and Semi- Supervised Learning on Large Graphs,' Proc. Ann. Conf. Learning Theory (COLT), pp. 624-638, 2004. (Pubitemid 38940364)
5. M. Belkin, P. Niyogi, and V. Sindhwani, 'Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples,' J. Machine Learning Research, vol. 7, pp. 2399-2434, 2006. (Pubitemid 44708005)
6. T. Cootes, G. Edwards, and C. Taylor, 'Active Appearance Models,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681-685, June 2001. (Pubitemid 32585279)
7. D. Cai, X. He, and J. Han, 'Semi-Supervised Discriminant Analysis,' Proc. IEEE Int'l Conf. Computer Vision, pp. 1-7, 2007.
8. Y. Fu and T. Huang, 'Human Age Estimation with Regression on Discriminative Aging Manifold,' IEEE Trans. Multimedia, vol. 10, no. 4, pp. 578-584, June 2008. (Pubitemid 351711234)
9. G. Guo, Y. Fu, C. Dyer, and T. Huang, 'Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression,' IEEE Trans. Image Processing, vol. 17, no. 7, pp. 1178- 1188, July 2008.
10. J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdino, 'Neighborhood Component Analysis,' Advances in Neural Information Processing Systems, pp. 513-520, MIT Press, 2004.
11. X. Geng, Z. Zhou, and K. Smith-Miles, 'Automatic Age Estimation Based on Facial Aging Patterns,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2234-2240, Dec. 2007. (Pubitemid 350124070)
12. J. Hayashi, M. Yasumoto, H. Ito, and H. Koshimizu, 'A Method for Estimating and Modeling Age and Gender Using Facial Image Processing,' Proc. Int'l Conf. Virtual Systems and Multimedia, pp. 439-448, 2001.
13. J. Hull, 'A Database for Handwritten Text Recognition Research,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 5, pp. 550-554, May 1994.
14. I. Joliffe, Principal Component Analysis. Springer-Verlag, 1986.
15. Y. Kwon and N. Lobo, 'Age Classification from Facial Images,' Computer Vision and Image Understanding, vol. 74, no. 1, pp. 1-21, 1999.
16. A. Lanitis, C. Draganova, and C. Christodoulou, 'Comparing Different Classifiers for Automatic Age Estimation,' IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 34, no. 1, pp. 621-628, Feb. 2004.
17. B. Ni, S. Yan, A. Kassim, and L. Cheong, 'Learning by Propagability,' Proc. Int'l Conf. Data Mining, pp. 492-501, 2008.
18. W. Rudin, Principles of Math. Analysis, third ed. McGray-Hill, 1976.
19. D. Rumelhart, G. Hinton, and R. Williams, 'Learning Representations by Back-Propagating Errors,' Nature, vol. 323, pp. 533-536, 1986. (Pubitemid 16025374)
20. R. Salakhutdinov and G. Hinton, 'Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure,' Proc. Int'l Conf. Artificial Intelligence and Statistics, 2007.
21. T. Sim, S. Baker, and M. Bsat, 'The CMU Pose, Illumination, and Expression Database,' Proc. European Conf. Computer Vision, vol. 25, pp. 1615-1618, 2003.
22. E. Xing, A. Ng, M. Jordan, and S. Russell, 'Distance Metric Learning, with Application to Clustering with Side-Information,' Advances in Neural Information Processing Systems, pp. 505-512, MIT Press, 2003.
23. L. Yang, R. Jin, R. Sukthankar, and Y. Liu, 'An Efficient Algorithm for Local Distance Metric Learning,' Proc. Nat'l Conf. Artificial Intelligence, vol. 21, no. 1, pp. 543-548, 2006. (Pubitemid 44705340)
24. J. Yan, B. Zhang, N. Liu, S. Yan, Q. Cheng, W. Fan, Q. Yang, W. Xi, and Z. Chen, 'Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing,' IEEE Trans. Knowledge and Data Eng., vol. 18, no. 3, pp. 320-333, Mar. 2006.
25. S. Yan, H. Wang, T. Huang, Q. Yang, and X. Tang, 'Ranking with Uncertain Labels,' Proc. IEEE Int'l Conf. Multimedia and Expo, pp. 96-99, 2007.
26. S. Yan, H. Wang, X. Tang, J. Liu, and T. Huang, 'Regression from Uncertain Labels and Its Applications to Soft-Biometrics,' IEEE Trans. Information Forensics and Security, vol. 3, no. 4, pp. 698-708, Dec. 2008.
27. S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, and T. Huang, 'Regression from Patch-Kernel,' Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
28. D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Scholkopf, 'Learning with Local and Global Consistency,' Proc. Neural Information Processing Systems, 2003.
29. X. Zhu, Z. Ghahramani, and J. Lafferty, 'Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions,' Proc. Int'l Conf. Machine Learning, vol. 2, pp. 912-919, 2003.
30. X. Zhu, 'Semi-Supervised Learning Literature Survey,' Technical Report 1530, Computer Sciences, Univ. of Wisconsin, 2006.