联合局部学习和组稀疏回归的无监督特征选择

吴越; 王灿; 章越清; 卜佳俊

doi:10.1631/FITEE.1700804

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联合局部学习和组稀疏回归的无监督特征选择

常规文章 | Updated：2022-05-19

- 联合局部学习和组稀疏回归的无监督特征选择
- Unsupervised feature selection via joint local learning and group sparse regression
- 信息与电子工程前沿（英文） 2019年20卷第4期页码：538-553
- Affiliations：
  
  Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou 310027, China
- Author bio：
  
  [ "Yue WU, E-mail: wy1988@zju.edu.cn" ]
  Can WANG, E-mail: wcan@zju.edu.cn
  [ "Yue-qing ZHANG, E-mail: 704787221@qq.com" ]
  [ "Jia-jun BU, E-mail: bjj@zju.edu.cn" ]
- Funds：
  
  Project supported by Alibaba-Zhejiang University Joint Institute of Frontier Technologies and Zhejiang Provincial Key Research and Development Plan (No. 2017C01012)
- DOI：10.1631/FITEE.1700804
  中图分类号： TP391.4
- 收稿：2017-12-02，
  
  修回：2019-;4-11，
  
  纸质出版：2019-04
- Accepted：
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吴越, 王灿, 章越清, 等. 联合局部学习和组稀疏回归的无监督特征选择[J]. 信息与电子工程前沿（英文）, 2019,20(4):538-553.

Yue WU, Can WANG, Yue-qing ZHANG, et al. Unsupervised feature selection via joint local learning and group sparse regression[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(4): 538-553.
吴越, 王灿, 章越清, 等. 联合局部学习和组稀疏回归的无监督特征选择[J]. 信息与电子工程前沿（英文）, 2019,20(4):538-553. DOI： 10.1631/FITEE.1700804.

Yue WU, Can WANG, Yue-qing ZHANG, et al. Unsupervised feature selection via joint local learning and group sparse regression[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(4): 538-553. DOI： 10.1631/FITEE.1700804.

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。

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.

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