BMVC 2004, Kingston, 7th-9th Sept, 2004

Non-Linear Feature Selection for Classification
M. Brown (UMIST) and N.P. Costen (Manchester Metropolitan University)

This paper addresses the issues associated with performing feature or parameter
selection for non-linear classifiers using a basis pursuit regularization
framework. New results on representing the feature selection problem as a
primal/dual calculation for both hard and soft margin classification problems
are derived, and it is shown that optimal feature selection can be posed, in
dual form, as a set of 2n linear inequality constraints. While this is efficient,
it does limit the technique to non-linear kernels that have a finite expansion,
such as polynomials. The issues associated with both efficiently calculating
a polynomial basis pursuit classifier are then addressed and the technique is
shown to improve discrimination performance on the MNIST digit set.
(pdf article)