Multiclass Diffuse Interface Models for Semi-supervised Learning on Graphs
Cristina Garcia-Cardona, Arjuna Flenner, Allon G. Percus
2013
Abstract
We present a graph-based variational algorithm for multiclass classification of high-dimensional data, motivated by total variation techniques. The energy functional is based on a diffuse interface model with a periodic potential. We augment the model by introducing an alternative measure of smoothness that preserves symmetry among the class labels. Through this modification of the standard Laplacian, we construct an efficient multiclass method that allows for sharp transitions between classes. The experimental results demonstrate that our approach is competitive with the state of the art among other graph-based algorithms.
References
- Allwein, E. L., Schapire, R. E., and Singer, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1:113-141.
- Bertozzi, A., Esedo g¯lu, S., and Gillette, A. (2007). Inpainting of binary images using the Cahn-Hilliard equation. IEEE Transactions on Image Processing, 16(1):285- 291.
- Bertozzi, A. L. and Flenner, A. (2012). Diffuse interface models on graphs for classification of high dimensional data. Multiscale Modeling and Simulation, 10(3):1090-1118.
- Bühler, T. and Hein, M. (2009). Spectral clustering based on the graph p-Laplacian. In Bottou, L. and Littman, M., editors, Proceedings of the 26th International Conference on Machine Learning, pages 81-88. Omnipress, Montreal, Canada.
- Chung, F. R. K. (1997). Spectral graph theory. In Regional Conference Series in Mathematics, volume 92. Conference Board of the Mathematical Sciences (CBMS), Washington, DC.
- Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., and Zucker, S. W. (2005). Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proceedings of the National Academy of Sciences, 102(21):7426-7431.
- Dietterich, T. G. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2(1):263- 286.
- Dobrosotskaya, J. A. and Bertozzi, A. L. (2008). A waveletlaplace variational technique for image deconvolution and inpainting. IEEE Trans. Image Process., 17(5):657-663.
- Dobrosotskaya, J. A. and Bertozzi, A. L. (2010). Wavelet analogue of the Ginzburg-Landau energy and its gamma-convergence. Interfaces and Free Boundaries, 12(2):497-525.
- Gilboa, G. and Osher, S. (2008). Nonlocal operators with applications to image processing. Multiscale Modeling and Simulation, 7(3):1005-1028.
- Har-Peled, S., Roth, D., and Zimak, D. (2003). Constraint classification for multiclass classification and ranking. In S. Becker, S. T. and Obermayer, K., editors, Advances in Neural Information Processing Systems 15, pages 785-792. MIT Press, Cambridge, MA.
- Hastie, T. and Tibshirani, R. (1998). Classification by pairwise coupling. In Advances in Neural Information Processing Systems 10. MIT Press, Cambridge, MA.
- Hein, M. and Setzer, S. (2011). Beyond spectral clustering - tight relaxations of balanced graph cuts. In Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., and Weinberger, K., editors, Advances in Neural Information Processing Systems 24, pages 2366-2374.
- Jung, Y. M., Kang, S. H., and Shen, J. (2007). Multiphase image segmentation via Modica-Mortola phase transition. SIAM J. Appl. Math, 67(5):1213-1232.
- Kohn, R. V. and Sternberg, P. (1989). Local minimizers and singular perturbations. Proc. Roy. Soc. Edinburgh Sect. A, 111(1-2):69-84.
- Li, Y. and Kim, J. (2011). Multiphase image segmentation using a phase-field model. Computers and Mathematics with Applications, 62:737-745.
- Liu, W., He, J., and Chang, S.-F. (2010). Large graph construction for scalable semi-supervised learning. Proceedings of the 27th International Conference on Machine Learning.
- Subramanya, A. and Bilmes, J. (2011). Semi-supervised learning with measure propagation. Journal of Machine Learning Research, 12:3311-3370.
- Szlam, A. and Bresson, X. (2010). Total variation and cheeger cuts. In Fürnkranz, J. and Joachims, T., editors, Proceedings of the 27th International Conference on Machine Learning, pages 1039-1046. Omnipress, Haifa, Israel.
- Szlam, A. D., Maggioni, M., and Coifman, R. R. (2008). Regularization on graphs with function-adapted diffusion processes. Journal of Machine Learning Research, 9:1711-1739.
- von Luxburg, U. (2006). A tutorial on spectral clustering. Technical Report TR-149, Max Planck Institute for Biological Cybernetics.
- Wang, J., Jebara, T., and Chang, S.-F. (2008). Graph transduction via alternating minimization. Proceedings of the 25th International Conference on Machine Learning.
- Zelnik-Manor, L. and Perona, P. (2005). Self-tuning spectral clustering. In Saul, L. K., Weiss, Y., and Bottou, L., editors, Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA.
- Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Schölkopf, B. (2004). Learning with local and global consistency. In Thrun, S., Saul, L. K., and Schölkopf, B., editors, Advances in Neural Information Processing Systems 16, pages 321-328. MIT Press, Cambridge, MA.
- Zhou, D. and Schölkopf, B. (2004). A regularization framework for learning from graph data. In Workshop on Statistical Relational Learning. International Conference on Machine Learning, Banff, Canada.
Paper Citation
in Harvard Style
Garcia-Cardona C., Flenner A. and G. Percus A. (2013). Multiclass Diffuse Interface Models for Semi-supervised Learning on Graphs . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 78-86. DOI: 10.5220/0004268100780086
in Bibtex Style
@conference{icpram13,
author={Cristina Garcia-Cardona and Arjuna Flenner and Allon G. Percus},
title={Multiclass Diffuse Interface Models for Semi-supervised Learning on Graphs},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={78-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004268100780086},
isbn={978-989-8565-41-9},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Multiclass Diffuse Interface Models for Semi-supervised Learning on Graphs
SN - 978-989-8565-41-9
AU - Garcia-Cardona C.
AU - Flenner A.
AU - G. Percus A.
PY - 2013
SP - 78
EP - 86
DO - 10.5220/0004268100780086