TY - JOUR
T1 - A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization
AU - Nie, Feiping
AU - Yang, Sheng
AU - Zhang, Rui
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - auto-weighted
KW - Feature selection
KW - redundancy minimization
KW - redundant features
UR - http://www.scopus.com/inward/record.url?scp=85058893811&partnerID=8YFLogxK
U2 - 10.1109/TIP.2018.2886761
DO - 10.1109/TIP.2018.2886761
M3 - 文章
AN - SCOPUS:85058893811
SN - 1057-7149
VL - 28
SP - 2428
EP - 2438
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 5
M1 - 8576636
ER -