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

Feiping Nie, Sheng Yang, Rui Zhang, Xuelong Li

Abstract: 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 feature selection framework via global redundancy minimization (AGRM) in this paper. Different from other feature selection methods, the proposed method can truly select the representative and non-redundant features, since the redundancy among the features can be largely reduced from the global perspective. In addition, AGRM is extended to a compact framework, which is more concise and efficient. Moreover, both the proposed frameworks are auto-weighted, i.e., parameter-free, so that they are pragmatic in real applications. In general, the proposed frameworks serve as a post-processing system, which can be applied to the existing supervised and unsupervised feature selection methods to refine the original feature score for the non-redundant features. Eventually, extensive experiments on nine benchmark datasets are conducted to demonstrate the effectiveness and the superiority of our proposed frameworks.