Data driven constraints for the SVM

TY - GEN

T1 - Data driven constraints for the SVM

AU - Darkner, Sune

AU - Clemmensen, Line H.

N1 - Conference code: 3

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-35428-1_9

DO - 10.1007/978-3-642-35428-1_9

M3 - Article in proceedings

SN - 978-3-642-35427-4

T3 - Lecture notes in computer science

SP - 70

EP - 77

BT - Machine Learning in Medical Imaging

A2 - Wang, Fei

A2 - Shen, Dinggang

A2 - Yan, Pingkun

A2 - Suzuki, Kenji

PB - Springer

T2 - 3rd International Workshop on Machine Learning in Medical Imaging

Y2 - 1 October 2012 through 1 October 2012

ER -