A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization.

Most existing feature selection methods rank all the features by a certain criterion via which the top ranking features are selected for the subsequent classification or clustering tasks. Due to neglecting the feature redundancy, the selected features are frequently correlated with each other such that the performance could be compromised. To address this issue, we propose a novel auto-weighted fe...