## Data Driven Constraints for the SVM

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Standard

**Data Driven Constraints for the SVM.** / Darkner, Sune; Clemmensen, Line Katrine Harder.

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Harvard

*Machine Learning in Medical Imaging: Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers.*Springer, pp. 70-77. Lecture Notes in Computer Science, vol. 7588, , 10.1007/978-3-642-35428-1_9

### APA

*Machine Learning in Medical Imaging: Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers.*(pp. 70-77). Springer. (Lecture Notes in Computer Science, Vol. 7588). 10.1007/978-3-642-35428-1_9

### CBE

### MLA

*Machine Learning in Medical Imaging: Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers.*Springer. 2012. 70-77. (Lecture Notes in Computer Science, Volume 7588). Available: 10.1007/978-3-642-35428-1_9

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### Author

### Bibtex

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### RIS

TY - GEN

T1 - Data Driven Constraints for the SVM

A1 - Darkner,Sune

A1 - Clemmensen,Line Katrine Harder

AU - Darkner,Sune

AU - Clemmensen,Line Katrine Harder

PB - Springer

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.

KW - SVM

KW - Regularization

KW - Kernels

KW - Classifier design

KW - Shape

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

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

SN - 978-3-642-35427-4

BT - Machine Learning in Medical Imaging

T2 - Machine Learning in Medical Imaging

T3 - Lecture Notes in Computer Science

T3 - en_GB

SP - 70

EP - 77

ER -