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On the role of sparsity in feature selection and an innovative method LRMI
Yuchun Fang; Qiulong Yuan; Zhaoxiang Zhang
发表期刊Neurocomputing
2018
期号321页码:237-250
摘要

Feature selection is used in many applications in machine learning and bioinformatics. As a popular approach, feature selection can be implemented in the filter-manner based on the sparse solution of the l1 regularization. Most study of the l1 regularization concentrates on investigating the iteration solution of the problem or focuses on adapting sparsity to different applications. It is necessary to explore more deeply about how the sparsity learned with the l1 regularization contributes to feature selection. In this paper, we make an effort to analyze the role of the l1 regularization in feature selection from the perspective of information theory. We discover that the l1 regularization contributes to minimizing the redundancy in feature selection. To avoid the complex computation of the l1 optimization, we propose a novel feature selection algorithm, i.e. the Laplacian regularization based on mutual information (LRMI), which realizes the minimization of the redundancy in a new way, and incorporates the l2 norm to achieve automatic grouping. Extensive experimental results demonstrate the superiority of LRMI over several traditional l1 regularization based feature selection algorithms with less time consumption.

关键词Feature Selection L1 Regularization Information Theory Mutual Information
语种英语
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/23226
专题个人空间
推荐引用方式
GB/T 7714
Yuchun Fang,Qiulong Yuan,Zhaoxiang Zhang. On the role of sparsity in feature selection and an innovative method LRMI[J]. Neurocomputing,2018(321):237-250.
APA Yuchun Fang,Qiulong Yuan,&Zhaoxiang Zhang.(2018).On the role of sparsity in feature selection and an innovative method LRMI.Neurocomputing(321),237-250.
MLA Yuchun Fang,et al."On the role of sparsity in feature selection and an innovative method LRMI".Neurocomputing .321(2018):237-250.
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