A discriminative multi-class feature selection method via weighted l2,1 -norm and Extended Elastic Net
A discriminative multi-class feature selection method via weighted l2,1 -norm and Extended Elastic Net
复制标题
通过加权l(2,1)-范数和扩展弹性网络的判别性多类特征选择方法
DOI：
10.1016/j.neucom.2017.09.055
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发表时间：
2018-01-31
期刊：
影响因子：
6
通讯作者：
Luo, Bin
中科院分区：
文献类型：
--
作者：
Chen, Si-Bao;Zhang, Ying;Luo, Bin
特征选择在许多模式识别和机器学习应用中扮演着重要的角色，其中期望从高维原始数据中提取有意义的特征，并期望消除噪声。基于l（2，1）-范数正则化的鲁棒特征选择（Robust Feature Selection，RFS）算法由于其高效性和高联合稀疏性而备受关注。在本文中，我们提出了一个更一般的框架，强大的和歧视性的多类特征选择。采用四种基于特征和标签之间相关性的加权方法来增强l（2，1）-范数联合稀疏度的鉴别性能。为了提高算法的稳定性，在多类弹性网的基础上引入了F范数正则化。给出了一个有效的算法及其收敛性证明。在两类和多类数据集上的实验结果验证了该方法的有效性。(C)2017爱思唯尔B. V.保留所有权利。
Feature selection has playing an important role in many pattern recognition and machine learning applications, where meaningful features are desired to be extracted from high dimensional raw data and noisy ones are expected to be eliminated. l(2,1)-norm regularization based Robust Feature Selection (RFS) has extracted a lot of attention due to its efficiency and high performance of joint sparsity. In this paper, we propose a more general framework for robust and discriminative multi-class feature selection. Four types of weighting, which are based on correlation information between features and labels, are adopted to strengthen the discriminative performance of l(2,1)-norm joint sparsity. F-norm regularization, which is extended from multi-class Elastic Net, is added to improve the stability of the method. An efficient algorithm and its corresponding convergence proof are provided. Experimental results on several twoclass and multi-class datasets are performed to verify the effectiveness of the proposed feature selection method. (C) 2017 Elsevier B.V. All rights reserved.