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A Sparsity Driven Kernel Machine Based on
Minimizing a Generalization Error Bound
Dori Peleg and Ron Meir
Pattern Recognition
Volume In press,
2008.
AbstractA new sparsity driven kernel classifier is presented based on the minimization of
a recently derived data-dependent generalization error bound. The objective function
consists of the usual hinge loss function penalizing training errors and a concave
penalty function of the expansion coefficients. The problem of minimizing the
non-convex bound is addressed by a successive linearization approach, whereby the
problem is transformed into a sequence of linear programs. The algorithm produced
comparable error rates to the standard Support Vector Machine but significantly
reduced the number of support vectors and the concomitant classification time. [Edit]
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