Data Driven Constraints for the SVM

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    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.
    Original languageEnglish
    Title of host publicationMachine Learning in Medical Imaging : Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers
    PublisherSpringer
    Publication date2012
    Pages70-77
    ISBN (Print)978-3-642-35427-4
    ISBN (Electronic)978-3-642-35428-1
    DOIs
    Publication statusPublished - 2012
    Event3rd International Workshop Machine Learning in Medical Imaging (MLMI 2012) - Nice, France
    Duration: 1 Oct 2012 → …
    http://miccai-mlmi.uchicago.edu/

    Workshop

    Workshop3rd International Workshop Machine Learning in Medical Imaging (MLMI 2012)
    CountryFrance
    CityNice
    Period01/10/2012 → …
    Internet address
    SeriesLecture Notes in Computer Science
    Volume7588
    ISSN0302-9743

    Keywords

    • SVM
    • Regularization
    • Kernels
    • Classifier design
    • Shape

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