论文|Jian Wang, Hua-Qing Zhang, Jun-Ze Wang, Yi-Fei PU*：Feature Selection using a Neural Network with Group Lasso Regularization and Controlled Redundancy

本文（Feature Selection using a Neural Network with Group Lasso Regularization and Controlled Redundancy）原载IEEE Transactions on Neural Networks and Learning Systems，由四川大学计算机学院蒲亦非教授等科研人员创作，系四川大学“智慧法治”超前部署学科系列学术成果。后续会持续分享四川大学“智慧法治”超前部署学科系列学术成果，欢迎大家阅读。

Abstract:We propose a neural network-based feature selection (FS) scheme that can control the level of redundancy in the selected features by integrating two penalties into a single objective function. The Group Lasso penalty aims to produce sparsity in features in a grouped manner. The redundancy-control penalty, which is defined based on a measure of dependence among features, is utilized to control the level of redundancy among the selected features. Both the penalty terms involve the L2,1-norm of weight matrix between the input and hidden layers. These penalty terms are nonsmooth at the origin, and hence, one simple but efficient smoothing technique is employed to overcome this issue. The monotonicity and convergence of the proposed algorithm are specified and proved under suitable assumptions. Then, extensive experiments are conducted on both artificial and real data sets. Empirical results explicitly demonstrate the ability of the proposed FS scheme and its effectiveness in controlling redundancy. The empirical simulations are observed to be consistent with the theoretical results.

Jian Wang, Hua-Qing Zhang, Jun-Ze Wang, Yi-Fei PU*, and Nikhil R. Pal. “Feature Selection using a Neural Network with Group Lasso Regularization and Controlled Redundancy,”IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1110-1123, 2021.（论文下载）