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k-support reguralized Support Vector Machine
The k-support regularized Support Vector Machine (ksup-SVM)
is a spasity regularization method that balances the L1
and L2 norms over a linear function in order to prevent
overfilling. This approach enables the learning algorithm
to select a sparse but correlated subset of discriminative
variables. It has two input parameters, the λ > 0
regularization parameter and k ∈ { 1 , . . . , d }, where
d is the dimension of the feature space, the parameter that
negatively correlates with the sparsity. Furthermore, the
k sup-SVM can be seen as an alternative to the Elastic Net
regularized SVM, but with a tighter convex relaxation to
correlated sparsity. We evaluated our proposed method in a
neurmuscular disease classification task using MRI-based
markers.
Publications
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Sparse classification with MRI based markers
for neuromuscular disease categorization.
Gkirtzou Katerina, Deux Jean-François, Bassez Guillaume, Sotiras Aristeidis, Rahmouni Alain, Varacca Thibault,Paragios Nikos and Blaschko B. Matthew
4th International Workshop on Machine Learning in Medical Imaging (MLMI), 2013.
[pdf] [Supplementary Material] [bibtex] [doi] -
Sparsity regularization and graph-based
representation in medical imaging
Gkirtzou Katerina
PhD Thesis, Ecole Centrale de Paris, 2013.
[pdf] [bibtex] [presentation]