Full Text:   <559>

Summary:  <131>

CLC number: TP391.4

On-line Access: 2019-05-14

Received: 2017-12-02

Revision Accepted: 2018-03-09

Crosschecked: 2019-04-11

Cited: 0

Clicked: 2787

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Can Wang

http://orcid.org/0000-0002-5890-4307

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.4 P.538-553

http://doi.org/10.1631/FITEE.1700804


Unsupervised feature selection via joint local learning and group sparse regression


Author(s):  Yue Wu, Can Wang, Yue-qing Zhang, Jia-jun Bu

Affiliation(s):  Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   wy1988@zju.edu.cn, wcan@zju.edu.cn, 704787221@qq.com, bjj@zju.edu.cn

Key Words:  Unsupervised, Local learning, Group sparse regression, Feature selection


Yue Wu, Can Wang, Yue-qing Zhang, Jia-jun Bu. Unsupervised feature selection via joint local learning and group sparse regression[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(4): 538-553.

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author="Yue Wu, Can Wang, Yue-qing Zhang, Jia-jun Bu",
journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700804"
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%A Jia-jun Bu
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DOI - 10.1631/FITEE.1700804


Abstract: 
feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However, due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression (JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets (images, voices, and web pages) demonstrate the effectiveness of JLLGSR.

联合局部学习和组稀疏回归的无监督特征选择

摘要:近十年,特征选择备受关注。通过挑选特征子集,可有效提升学习算法效率。由于难以获取标签信息,无监督特征选择算法相较于有监督特征选择算法应用更为广泛,其关键在于找出更能反映数据分布的特征集合。由于数据集中存在冗余和噪声,使用全部特征并不能很好展现数据的真实分布。为解决这一问题,本文提出联合局部学习和组稀疏回归的无监督特征选择算法。将基于局部学习聚类方法与组稀疏回归算法有机整合,选出有效反映数据流形分布同时保持组稀疏结构的特征。通过迭代算法,回归系数汇聚到重要特征上,选出能得到更优聚类效果的特征。对多个实际数据集(图像、声音和网页)的实验证明了该算法的有效性。

关键词:无监督;局部学习;组稀疏回归;特征选择

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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