Ravi Kumar, Jasmine Novak, et al.
Communications of the ACM
Motivated by frequently recurring themes in information retrieval and related disciplines, we define a genre of problems called combinatorial feature selection problems. Given a set S of multidimensional objects, the goal is to select a subset K of relevant dimensions (or features) such that some desired property Π holds for the set S restricted to K. Depending on Π, the goal could be to either maximize or minimize the size of the subset K. Several well-studied feature selection problems can be cast in this form. We study the problems in this class derived from several natural and interesting properties Π, including variants of the classical p-center problem as well as problems akin to determining the VC-dimension of a set system. Our main contribution is a theoretical framework for studying combinatorial feature selection, providing (in most cases essentially tight) approximation algorithms and hardness results for several instances of these problems.
Ravi Kumar, Jasmine Novak, et al.
Communications of the ACM
Miklós Ajtai, T.S. Jayram, et al.
STOC 2002
Shuchi Chawla, Robert Krauthgamer, et al.
Computational Complexity
Ravi Kumar, Prabhakar Raghavan, et al.
Computer Networks