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Learning a Propagable Graph for Semisupervised Learning: Classification and Regression
January 2012 (vol. 24 no. 1)
pp. 114-126
ASCII Text
x 
 
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, "Learning a Propagable Graph for Semisupervised Learning: Classification and Regression," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 114-126, January, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2010.209,
author = {Bingbing Ni and Shuicheng Yan and Ashraf A. Kassim},
title = {Learning a Propagable Graph for Semisupervised Learning: Classification and Regression},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {1},
issn = {1041-4347},
year = {2012},
pages = {114-126},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.209},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Learning a Propagable Graph for Semisupervised Learning: Classification and Regression
IS - 1
SN - 1041-4347
SP114
EP126
EPD - 114-126
A1 - Bingbing Ni,
A1 - Shuicheng Yan,
A1 - Ashraf A. Kassim,
PY - 2012
KW - Semi-supervised learning
KW - graph-based learning
KW - feature extraction
KW - and similarity graph.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Bingbing Ni, Advanced Digital Sciences Center, Singapore
Shuicheng Yan, National University of Singapore, Singapore
Ashraf A. Kassim, National University of Singapore, Singapore
In this paper, we present a novel framework, called learning by propagability, for two essential data mining tasks, i.e., classification and regression. The whole learning process is driven by the philosophy that the data labels and the optimal feature representation jointly constitute a harmonic system, where the data labels are invariant with respect to the propagation on the similarity graph constructed based on the optimal feature representation. Based on this philosophy, a unified framework of learning by propagability is proposed for the purposes of both classification and regression. Specifically, this framework has three characteristics: 1) the formulation unifies the label propagation and optimal feature representation pursuing, and thus the label propagation process is enhanced by benefiting from the refined similarity graph constructed with the derived optimal feature representation instead of the original representation; 2) it unifies the formulations for supervised and semisupervised learning in both classification and regression tasks; and 3) it can directly deal with the multiclass classification problems. Extensive experiments for the classification task on UCI toy data sets, handwritten digit recognition, face recognition, and microarray recognition as well as for the regression task of human age estimation on the FG-NET aging database, all validate the effectiveness of our proposed learning framework, compared with the state-of-the-art counterparts.

[1] The Fg-Net Aging Database: http://sting.cycollege.ac.cy/ ~alanitisfgnetaging.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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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. Sch¨olkopf, "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.

Index Terms:
Semi-supervised learning, graph-based learning, feature extraction, and similarity graph.
Citation:
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, "Learning a Propagable Graph for Semisupervised Learning: Classification and Regression," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 114-126, Jan. 2012, doi:10.1109/TKDE.2010.209
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