Abstract
We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time. Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use of the ℓ1-norm on the constraint while still allowing for the application of kernels. We specialize the proposed constraint to orthogonality of the vectors between paired observations and the estimated hyperplane. We show that imposing the constraint of orthogonality on the paired data yields a more robust classifier solution, compared to the SVM i.e. reduces variance and improves classification rates. We present a quantitative measure of the information level contained in the pairing and test the method on simulated as well as a high-dimensional paired data set of ear-canal surfaces.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aizerman, M., Braverman, E., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)
Darkner, S., Larsen, R., Paulsen, R.R.: Analysis of Deformation of the Human Ear and Canal Caused by Mandibular Movement. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 801–808. Springer, Heidelberg (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons (2001)
Golland, P.: Discriminative direction for kernel classifiers. In: NIPS, pp. 745–752 (2001)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer (2001)
Hoerl, A.E., Kennard, R.W.: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970)
Karush, W.: Minima of Functions of Several Variables with Inequalities as Side Constraints. Master’s thesis, Univ. of Chicago (1939)
Li, F., Yang, Y., Xing, E.: From lasso regression to feature vector machine. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems 18, pp. 779–786. MIT Press, Cambridge (2006)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, UK (2004)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B 58(1), 267–288 (1996)
Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., Knight, K.: Sparsity and smoothness via the fused lasso. J. R. Statist. Soc. B 1(67), 91–106 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)
Wang, L., Zhu, J., Zou, H.: The doubly regularized support vector machine. Statistica Sinica 16, 589–615 (2006)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Statist. Soc. B 67(pt. 2), 301–320 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Darkner, S., Clemmensen, L.H. (2012). Data Driven Constraints for the SVM. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-35428-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35427-4
Online ISBN: 978-3-642-35428-1
eBook Packages: Computer ScienceComputer Science (R0)