P. S Bradley and O. L Mangasarian (1998)
Feature Selection via Concave Minimization and Support Vector Machines
In: Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), ed. by J. Shavlik, pp. 82-90, San Francisco, California, Morgan Kaufmann.
Computational comparison is made between two feature selection approaches for finding a separating plane that discriminates between two point sets using as few of the features as possible. In concave minimization features are explicitly suppressed while in the support vector approach a p-norm distance between separating planes is maximized.