2014 EffectiveGlobalApproachesforMut

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Most current mutual information (MI) based feature selection techniques are greedy in nature thus are prone to sub-optimal decisions. Potential performance improvements could be gained by systematically posing MI-based feature selection as a global optimization problem. A rare attempt at providing a global solution for the MI-based feature selection is the recently proposed Quadratic Programming Feature Selection (QPFS) approach. We point out that the QPFS formulation faces several non-trivial issues, in particular, how to properly treat feature `self-redundancy' while ensuring the convexity of the objective function. In this paper, we take a systematic approach to the problem of global MI-based feature selection. We show how the resulting NP-hard global optimization problem could be efficiently approximately solved via spectral relaxation and semi-definite programming techniques. We experimentally demonstrate the efficiency and effectiveness of these novel feature selection frameworks.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 EffectiveGlobalApproachesforMutXuan Vinh Nguyen
Jeffrey Chan
Simone Romano
James Bailey
Effective Global Approaches for Mutual Information based Feature Selection10.1145/2623330.26236112014